Patent Abstract:
One embodiment of the present invention is directed to a computer-implemented system that analyzes free-form text comments provided by a user (such as a customer of a company) and draws conclusions about the tone of the user&#39;s feedback, such as whether the user&#39;s feedback is positive, negative, angry, critical, or congratulatory. Such conclusions may be reflected in a single numerical value referred to herein as “emotivity.” A customer&#39;s emotivity score may be used for various purposes, such as determining whether the customer is likely to provide a positive testimonial for the company, or whether a follow-up phone call should be made to the customer to improve the company&#39;s relationship with the customer. Furthermore, a measurement of the customer&#39;s loyalty to the company may be modified based on the user&#39;s measured emotivity.

Full Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is a continuation of U.S. patent application Ser. No. 12/758,349, filed on Apr. 12, 2010, entitled, “Emotivity and Vocality Measurement,” which claims priority from U.S. Provisional Pat. App. Ser. No. 61/168,618, filed on Apr. 12, 2009, entitled “Emotivity and Vocality Measurement,” both of which are hereby incorporated by reference herein. 
     
    
     BACKGROUND 
       [0002]    All businesses desire to increase the loyalty of their customers because it is well-recognized that increasing loyalty leads to increased profits. Most businesses, however, find increased customer loyalty to be an elusive goal. It is difficult to increase loyalty in a business or other relationship not only because it can be challenging to identify the concrete actions that need to be taken to increase such loyalty, but also because it can be difficult even to measure the current loyalty of a customer or other party to the relationship. Failure to obtain a concrete and objective measurement of current loyalty will almost certainly lead to an inability to identify those concrete actions which are likely to increase such loyalty most efficiently. 
         [0003]    Prior art techniques for measuring loyalty often require information about existing business relationships to be provided in the form of structured quantitative data, such as numerical answers to predetermined survey questions. Such techniques have limited usefulness, however, because it can be difficult and time-consuming to obtain such structured quantitative data from partners to a relationship. What is needed, therefore, are techniques for measuring loyalty based on unstructured and/or non-quantitative data, such as letters, email messages, blog entries, and other documents written by partners to a relationship. 
       SUMMARY 
       [0004]    One embodiment of the present invention is directed to a computer-implemented system that analyzes free-form text comments provided by a user (such as a customer of a company) and draws conclusions about the tone of the user&#39;s feedback, such as whether the user&#39;s feedback is positive, negative, angry, critical, or congratulatory. Such conclusions may be reflected in a single numerical value referred to herein as “emotivity.” A customer&#39;s emotivity score may be used for various purposes, such as determining whether the customer is likely to provide a positive testimonial for the company, or whether a follow-up phone call should be made to the customer to improve the company&#39;s relationship with the customer. Furthermore, a measurement of the customer&#39;s loyalty to the company may be modified based on the user&#39;s measured emotivity. 
         [0005]    In another embodiment of the present invention, a computer-implemented system analyzes free-form text comments provided by a user (such as a customer of a company) and draws conclusions about the opinions of the user based on the number of words in the user&#39;s comments, measured either as an absolute quantity or relative to a baseline, such as the average number of words in comments received from a plurality of users. A visual indication of the user&#39;s vocality may be displayed, such as a bullhorn with lines emanating from it, where the number of lines corresponds to the user&#39;s vocality. Furthermore, a measurement of the user&#39;s loyalty may be modified based on the user&#39;s vocality. 
         [0006]    Measures of vocality and emotivity may be presented relative to each other. For example, if the user&#39;s input indicates that he has a negative opinion of the other party to the relationship, then the user may be deemed a “detractor” of the other party. Conversely, if the user&#39;s input indicates that he has a positive opinion of the other party to the relationship, then the user may be deemed an “advocate” of the other party. Such conclusions about the user may be combined with the user&#39;s vocality score to produce labels for the user such as “Non-Vocal,” “Vocal” (e.g., if the user&#39;s input contains a large number of words that do not indicate either a positive or negative opinion of the other party), “Vocal Detractor” (if the user&#39;s input contains a large number of words indicating a negative opinion of the other party) and “Vocal Advocate” (if the user&#39;s input contains a large number of words indicating a positive opinion of the other party). 
         [0007]    For example, one embodiment of the present invention is directed to a computer-implemented method comprising: (A) providing a survey to a plurality of people, the survey comprising a plurality of questions; (B) receiving, from the plurality of people, a plurality of sets of answers to the plurality of questions; (C) identifying a plurality of loyalty indices of the plurality of people based on the plurality of sets of answers; (D) for each of the plurality of people U: (D) (1) identifying text input T associated with person U; (D)(2) identifying a count E of words in text input T which are in a set of words representing strong emotions; (D)(3) identifying a count P of words in text input T which are in a set of words representing positive emotions; (D)(4) identifying a count N of words in text input T which are in a set of words representing negative emotions; and (E) selecting values of coefficients A, B, and C that maximize a value of R 2  between the plurality of loyalty indices and values of a variable Emo for the plurality of people, wherein Emo=A*E+B*P+C*N. 
         [0008]    Another embodiment of the present invention is directed to a computer-implemented method comprising: (A) identifying a plurality of loyalty levels of a plurality of people; (B) identifying a plurality of text inputs provided by the plurality of people; (C) identifying a first subset of the plurality of people having loyalty levels satisfying a high loyalty level criterion; (D) identifying a second subset of the plurality of people having loyalty levels satisfying a low loyalty level criterion; (E) identifying a third subset of the plurality of people having loyalty levels not satisfying the high loyalty level criterion or the low loyalty level criterion; (F) identifying a first subset of the plurality of text inputs comprising text inputs provided by the first subset of the plurality of people and text inputs provided by the second subset of the plurality of people; (G) identifying a second subset of the plurality of text inputs comprising text inputs provided by the second subset of the plurality of people; and (H) identifying a third subset of the plurality of text inputs comprising the relative complement of the second subset of the plurality of text inputs relative to the first subset of the plurality of text inputs. 
         [0009]    Yet another embodiment of the present invention is directed to a computer-implemented method comprising: (A) identifying a set of words representing strong emotions; (B) identifying a set of words representing positive emotions; (C) identifying a set of words representing negative emotions; (D) identifying first text input T 1  associated with a person; (E) identifying a first count E 1  of the strong emotion words in text input I 1 ; (F) identifying a first count P 1  of the positive emotion words in text input I 1 ; (F) identifying a first count N 1  of the negative emotion words in text input I 1 ; and (G) identifying a first value V 1  representing an emotional content of text input I 1  based on E 1 , P 1 , and N 1 . 
         [0010]    Yet a further embodiment of the present invention is directed to a computer-implemented method comprising: (A) receiving, from a plurality of people, a plurality of text inputs having a plurality of sizes; (B) identifying a statistic derived from the plurality of sizes; (C) selecting one of the plurality of text inputs I 1  from one of the plurality of people P; (D) identifying a size of text input I 1 ; and (E) selecting a measurement V 1  associated with person P based on the size of text input I 1  and the statistic derived from the plurality of sizes. 
         [0011]    Another embodiment of the present invention is directed to a computer-implemented method comprising: (A) identifying text input T associated with a person; (B) counting a number of words W in the text input T; (C) providing, on an output device, a visual representation of W, comprising: (C)(1) identifying a range of values encompassing W; and (C)(2) identifying a visual representation corresponding to the range of values. 
         [0012]    Other features and advantages of various aspects and embodiments of the present invention will become apparent from the following description and from the claims. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0013]      FIG. 1  is a dataflow diagram of a system for calibrating a system for identifying the emotivity of a user according to one embodiment of the present invention; 
           [0014]      FIG. 2  is a flowchart of a method performed by the system of  FIG. 1  according to one embodiment of the present invention; 
           [0015]      FIG. 3  is a dataflow diagram of a system for generating a list of words connoting strong emotions according to one embodiment of the present invention; 
           [0016]      FIG. 4  is a flowchart of a method performed by the system of  FIG. 3  according to one embodiment of the present invention; 
           [0017]      FIG. 5  is a dataflow diagram of a system for generating an emotivity score of a user according to one embodiment of the present invention; 
           [0018]      FIG. 6  is a flowchart of a method performed by the system of  FIG. 5  according to one embodiment of the present invention; 
           [0019]      FIG. 7  is a dataflow diagram of a system for generating vocality scores for a plurality of users according to one embodiment of the present invention; 
           [0020]      FIGS. 8A-8C  are flowcharts of methods performed by the system of  FIG. 7  according to various embodiments of the present invention; 
           [0021]      FIGS. 9A-9D  are illustrations of icons representing vocality levels according to embodiments of the present invention; and 
           [0022]      FIG. 10  is a flowchart of a method for identifying trends in loyalty of a user over time according to one embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0023]    Certain embodiments of the present invention are directed to techniques for identifying a measure of emotion, referred to herein as “emotivity,” associated with text associated with a first person. For example, the first person may be a customer of a company. The customer may provide text related to the customer&#39;s relationship to the company in any of a variety of ways. For example, the customer may provide free-form text responses to a survey about the customer&#39;s relationship to the company. As other examples, embodiments of the invention may capture text from email messages, blog entries, word processing documents, or other text written by the user, whether or not such text was written with the intent that is be used by embodiments of the present invention. The text obtained by the system, whatever the source of that text may be, may be analyzed to measure the emotivity of the customer in relation to the company. The customer&#39;s emotivity may, for example, be used in measuring the customer&#39;s loyalty to the company. 
         [0024]    In one embodiment of the present invention, a single value representing the person&#39;s emotivity, referred to herein using the variable “Emotivity,” is calculated using a formula of the form represented by Equation 1: 
         [0000]      Emotivity= A*E count+ B*P count+ C*N count  Equation 1
 
         [0025]    In Equation 1, the variables A, B, and C are coefficients whose values must be initialized. Referring to  FIG. 1 , a dataflow diagram is shown of a system  100  that is used in one embodiment of the present invention to automatically generate values for coefficients A, B, and C. Referring to  FIG. 2 , a flowchart is shown of a method  200  that is performed by the system  100  of  FIG. 1  according to one embodiment of the present invention. A survey engine  102  provides a survey  104 , containing questions  104  relating to the practices and perceptions of partners to a relationship (such as business partners), to a plurality of users  106  ( FIG. 2 , step  202 ). The users  106  may, for example, be customers of a particular company. 
         [0026]    The survey  104  may include two kinds of questions: (1) questions  104 N calling for numeric responses, and (2) questions  104 T calling for free-text responses. Examples of techniques for providing surveys calling for numeric responses are disclosed in above-referenced patent application Ser. No. 61/168,618. As disclosed therein, the questions  104 N calling for numeric responses may, for example, each provide a statement and prompt the users  106  to provide a number indicating their degree of agreement with the statement. For example, a response of “1” may represent “Strongly Disagree,” while a response of “5” may represent “Strongly Agree.” The questions  104 N may be divided into groups of questions corresponding to different dimensions of loyalty, as disclosed in the above-referenced patent application. 
         [0027]    The questions  104 T calling for free-text responses may be provided within the survey  104  in any of a variety of ways. For example, each of the numeric questions  104 N may be followed by a prompt, such as “Comments:” or “Other:”, which calls for the users  106  to provide free-text input relating to the immediately-preceding numeric question. As another example, each group within the numeric questions (corresponding to a loyalty dimension) may be followed by a prompt which calls for the users  106  to provide free-text input relating to the immediately-preceding group of questions. These are merely examples of ways in which the free-text questions  104 T may be provided and do not constitute limitations of the present invention. 
         [0028]    The users  106  provide answers  108  to the surveys  104 . The answers  108  include both numeric answers  106 N and textual answers  106 T of the kinds described above. The answers  108  are received by a loyalty measurement engine  110  (step  204 ), which generates loyalty indices  112  for the users  106  based on the survey answers  108  (step  206 ). Examples of techniques that the loyalty measurement engine  110  may use to generate the loyalty indices  112  are described in the above-referenced patent application. In the particular embodiment illustrated in  FIG. 1 , the loyalty measurement engine  110  generates the loyalty indices  112  based solely on the numeric responses  108 N, although this is not a limitation of the present invention. 
         [0029]    A word count engine  114  counts, for each user, the total number of words in the user&#39;s answers, as well as the number of occurrences of words representing strong emotions (whether positive, negative, or otherwise), words representing positive emotions, and words representing negative emotions in each of the sets of survey answers  108  to produce words counts  116   a ,  116   b ,  116   c , and  116   d , respectively (step  208 ). Word counts  116   a  include, for each of the users  106 , a count of “strong emotion” words used by that user, referred to herein by the variable Ecount. Similarly, word counts  116   b  include, for each of the users  106 , a count of “positive emotion” words used by that user, referred to herein by the variable Pcount. Word counts  116   c  include, for each of the users  106 , a count of “negative emotion” words used by that user, referred to herein by the variable Ncount. Word counts  116   d  include, for each of the users  106 , a count of the total number of words used by that user. 
         [0030]    The word count engine  114  may generate the word counts  116  by entering a loop over each user U (step  210 ) and identifying text T associated with user U (step  212 ). The text T identified in step  212  may, for example, be the set of all textual responses provided by user U to the survey  104  (i.e., the portion of textual responses  108 T provided by user U) and/or otherwise captured by the system  100  (e.g., from blogs, word processing documents, and email messages). The word count engine  114  may then count, within text T, the total number of words used by user U (step  213 ). The word count engine  114  may then count, within text T, the number of words representing strong emotions to generate a value of Ecount for user U (step  214 ). The word count engine  114  may, for example, determine whether any particular word in text T represents a strong emotion by determining whether the word is contained within a predetermined list  122   a  (referred to herein as the “Emo list”) of words representing strong emotions. 
         [0031]    Similarly, the word count engine  114  may count, within text T, the number of words representing positive emotions to generate a value of Pcount for user U (step  216 ). The word count engine  114  may, for example, determine whether any particular word in text T represents a positive emotion by determining whether the word is contained within a predetermined list  122   b  (referred to herein as the “Positive list”) of words representing positive emotions. Finally, the word count engine  114  may count, within text T, the number of words representing negative emotions to generate a value of Ncount for user U (step  218 ). The word count engine  114  may, for example, determine whether any particular word in text T represents a negative emotion by determining whether the word is contained within a predetermined list  122   c  (referred to herein as the “Negative list”) of words representing negative emotions. The word count engine  114  may repeat steps  212 - 218  for the remaining users to complete the generation of counts  116   a ,  116   b , and  116   c  (step  220 ). 
         [0032]    The system  100  includes a calibration engine  118  which assigns values to a set of emotivity calibration parameters  120  based on the values of the word counts  116  (step  222 ). The calibration parameters  120  may include, for example, the coefficients A, B, and C of Equation 1 (represented in  FIG. 1  by elements  120   a ,  120   b , and  120   c ). In one embodiment, the calibration engine  120  assigns values to coefficients  120   a ,  120   b , and  120   c  that maximize the R 2  in a multivariate regression of the plurality of loyalty indices  112  against the values of Emotivity (calculated using Equation 1) for all users  106 . 
         [0033]    As described above, the method  200  of  FIG. 2  counts the number of occurrences of words that represent strong emotions, positive emotions, and negative emotions. As further described above, the method  200  may perform this function by using predetermined lists  122   a ,  122   b , and  122   c  of words representing strong, positive, and negative emotions, respectively. Referring to  FIG. 3 , a dataflow diagram is shown of a system  300  that is used in one embodiment of the present invention to generate the Emo list  122   a . Referring to  FIG. 4 , a flowchart is shown of a method  400  that is performed by the system  300  of  FIG. 3  according to one embodiment of the present invention. 
         [0034]    A loyalty level engine  306  identifies loyalty levels  308  of a plurality of users  302  based on input  304  provided by the users  302  ( FIG. 4 , step  402 ). Note that the users  302  in  FIG. 3  may be, but need not be, the same users  106  as those shown in  FIG. 1 . Furthermore, the input  304  in  FIG. 3  may be, but need not be, the survey responses  108  shown in  FIG. 1 . In the embodiment illustrated in  FIG. 3 , the input  304  includes both numeric input  304   a  and textual input  304   b , which may correspond to the numeric responses  108 N and the textual responses  108 T, respectively, shown in  FIG. 1 . Note, however, that the textual input  304   b  may come from sources in addition to or instead of the textual survey responses  108 T. For example, the textual input  304   b  may include word processing documents, email messages, web pages, or any other text created by or otherwise associated with the users  302 . Furthermore, the textual input  304   b  need not be provided by the users  302  at the same time as the non-textual input  304   a . For example, the users  302  may first provide the non-textual input  304   a , and later provide the textual input  304   b . Furthermore, the users  302  may provide different parts of the textual input  304   b  at different times. 
         [0035]    The loyalty level engine  306  may, for example, identify the loyalty levels  308  based solely on the numeric input  304   a . Examples of techniques that may be used by the loyalty level engine  306  to generate the loyalty levels  308  are disclosed in the above-referenced patent application Ser. No. 12/535,682. Furthermore, although the present discussion refers to loyalty levels, the techniques of  FIGS. 3 and 4  may be applied to loyalty indices, such as the kind disclosed in patent application Ser. No. 12/535,682. 
         [0036]    A loyalty level filter  310  identifies a set of loyalty levels  312   a  satisfying a predetermined high-loyalty level condition (step  404 ). For example, assume for purposes of the following discussion that the loyalty-level numbering scheme disclosed in the above-referenced patent application is used, in which there are four loyalty levels representing increasing degrees of loyalty in the following sequence: −1, 1, 2, and 3. In step  404 , users having a loyalty level of 3, for example, may be identified as the high-loyalty users  312   a.    
         [0037]    The loyalty level filter  310  also identifies a set of loyalty levels  312   b  satisfying a predetermined low-loyalty level condition (step  406 ). For example, users having a loyalty level of −1 according to the labeling scheme described above may be identified in step  406  as the low-loyalty users  312   b.    
         [0038]    The loyalty level filter  312  also identifies a set of loyalty levels  312   c  which do not satisfy either the high-loyalty or low-loyalty conditions (step  408 ). For example, users having a loyalty level of 1 or 2 according to the labeling scheme described above may be identified in step  408  as the “remainder” or “non-emotive” users  312   c.    
         [0039]    The system  300  identifies “emotive” users  316  as the union  314  of the high-loyalty level users  312   a  and low-loyalty level users  312   b  (step  410 ). 
         [0040]    Note that the loyalty levels  308  may include pointers (not shown) back to the text input  304   b  provided by the corresponding one of the users  302 . As a result, the various filtered loyalty levels  312   a - c  may be used to identify the corresponding text inputs  304   b  of the users having those loyalty levels. A text identifier  318  identifies a set of “emotive” text  320   a  as the set of all text input (in text input  304   b ) provided by users in the set of emotive users  316  (step  412 ). The text identifier identifies a set of “non-emotive” text  320   b  as the set of all text input (in text input  304   b ) provided by users in the set of non-emotive users  312   c  (step  414 ). 
         [0041]    The “Emo list”  122   a  is identified as the set of text which occurs in the emotive text  320   a  but not in the non-emotive text  320   b , in other words, as the relative complement  322  of the non-emotive text  320   b  in the emotive text  320   a  (step  416 ). 
         [0042]    The positive list  122   b  and negative list  122   c  ( FIG. 1 ) may also be generated in any of a variety of ways. For example, the positive list  122   b  may be generated by selecting an initial set of words (e.g., from a dictionary) representing positive emotions, and then expanding the initial list to create the positive list  122   b  by adding synonyms (e.g., from a thesaurus) of the initial set of positive words. Similarly, the negative list  122   c  may be generated by selecting an initial set of words (e.g., from a dictionary) representing negative emotions, and then expanding the initial list to create the negative list  122   c  by adding synonyms (e.g., from a thesaurus) of the initial set of negative words. The positive list  122   b  and/or negative list  122   c  may be customized in a variety of ways, such as by tailoring them to a particular industry and/or company. 
         [0043]    As mentioned above, various embodiments of the present invention may be used to generate an emotivity score for a user based on textual input provided by the user. For example, Equation 1 may be used to generate an emotivity score, represented by the value of the variable Emotivity, for a user based on the values of the coefficients A, B, and C, and the word counts ECount, PCount, and Ncount for that user. Referring to  FIG. 5 , a dataflow diagram is shown of a system  500  for generating an emotivity score  512  for a user  502  in this manner according to one embodiment of the present invention. Referring to  FIG. 6 , a flowchart is shown of a method  600  performed by the system  500  of  FIG. 5  according to one embodiment of the present invention. 
         [0044]    A user  502  provides textual input  504  to the system  500  ( FIG. 6 , step  602 ). The textual input  504  may take any form, such as free-form text responses to a survey, email messages, web pages, word processing documents, or any combination thereof. Note that if the textual input  504  is part of the free-text survey responses  108 T shown in  FIG. 1 , the calibration process illustrated in  FIGS. 1 and 2  may be integrated with the emotivity score generation process illustrated in  FIGS. 5 and 6 , such that the same set of user inputs  108  is used both to calibrate the system  100  and to generate emotivity scores for the users  106  of the system  100 . 
         [0045]    Note, however, that the user  502  need not have provided any of the survey responses  108  shown in  FIG. 1 . Furthermore, the emotivity score  512  of the user  502  may be identified using the system  500  of  FIG. 5  even if the loyalty level and/or loyalty index of the user  502  is unknown. All that is required from the user  502  to identify the user&#39;s emotivity score  512  is the user&#39;s textual input  504 . 
         [0046]    A word count engine  506  produces a count  508   a  of emotive words (step  604   a ), positive words (step  604   b ) and negative words (step  604   c ) in the users&#39; input  504 . The word count engine  506  may produce the word counts  508   a - c  by, for example, counting the frequencies of occurrence of words in the emo list  122   a , positive list  122   b , and negative list  122   c , respectively, in the user&#39;s textual input  504 . 
         [0047]    An emotivity engine  510  generates the emotivity score  512  for the user  502  based on the word counts  508   a - c  and the emotivity calibration parameters  120  shown in  FIG. 1  (step  606 ). As described above, the emotivity calibration parameters  120  may, for example, be the coefficients A, B, and C in Equation 1. Furthermore, the counts  508   a ,  508   b , and  508   c  may be the variables Ecount, PCount, and NCount in Equation 1. Therefore, the emotivity engine  510  may generate the emotivity score  512  for the user  502  by calculating Equation 1, which applies a linear weighting of the counts  508   a - c , using coefficients A, B, and C as weights. 
         [0048]    Various embodiments of the present invention may be used to measure the “vocality” of a user. The term “vocality,” as used herein, refers generally to the absolute and/or relative size of the input provided by the user, such as the number of words, characters, or sentences provided by the user in response to a survey. The user may, for example, provide such input in the form of typed free-form text, such as text provided in response to survey questions. The user may, however, provide such input in other ways, such as by selecting pre-written sentences or paragraphs from a library of text responses. 
         [0049]    The vocality of a particular user may, for example, be represented as a single number V, such as the number of words W in the user&#39;s input. A user&#39;s vocality may, however, be a function of W and/or other values derived from input provided by the user and/or other users. For example, once the number of words W provided by the user has been counted, the user&#39;s vocality V may be obtained as a function of W. Such a function may take any of a variety of forms. For example, the function may map some fixed number of non-overlapping ranges of W to the same number of vocality values. For example, the ranges W&lt;10, 10&lt;=W&lt;50, 50&lt;=W&lt;500, and 500&lt;=W may be mapped to four distinct values of V. Such vocality values may take any form, such as whole numbers (e.g., 1, 2, 3, and 4, respectively) or text labels, such as “Non-Vocal,” “Mildly Vocal,” “Very Vocal,” and “Extremely Vocal.” The user&#39;s vocality may be absolute or relative. For example, it may represent the absolute number of words in the user&#39;s input, or a relationship of the number of words in the user&#39;s input to a baseline, such as the average number of words in input received from a plurality of users. In the latter case, the user&#39;s vocality score may be represented in any of a variety of ways, such as a value representing the number of words (positive or negative) by which the number of words used by the user deviates from the baseline, the percentage (positive or negative) by which the number of words used by the user deviates from the baseline, or a range within which the user&#39;s vocality falls relative to the baseline (e.g., low, medium, or high). 
         [0050]    A user&#39;s vocality score may be combined with analysis of the content of the user&#39;s input. For example, if the user&#39;s input indicates that he has a negative opinion of the other party to the relationship, then the user may be deemed a “detractor” of the other party. Conversely, if the user&#39;s input indicates that he has a positive opinion of the other party to the relationship, then the user may be deemed an “advocate” of the other party. Such conclusions about the user may be combined with the user&#39;s vocality score to produce labels for the user such as “Non-Vocal,” “Vocal” (e.g., if the user&#39;s input contains a large number of words that do not indicate either a positive or negative opinion of the other party), “Vocal Detractor” (if the user&#39;s input contains a large number of words indicating a negative opinion of the other party) and “Vocal Advocate” (if the user&#39;s input contains a large number of words indicating a positive opinion of the other party). 
         [0051]    A user interface may be provided which displays information representing the user&#39;s vocality score. The visual display of the user&#39;s vocality score may take any of a variety of forms, such as the user&#39;s raw or normalized vocality score itself, the text label corresponding to the user&#39;s vocality score (e.g., “Vocal Advocate”), or a graphical icon representing the user&#39;s vocality score. For example, the graphical icon may be a megaphone from which zero or more lines emanate, where the number, size, or shape of the lines emanating from the megaphone correspond to the user&#39;s vocality score. Such an icon provides a visual indication of the user&#39;s vocality score that can be understood at a glance. 
         [0052]    Having described the concept of vocality generally, various techniques for measuring the vocality of one or more users will now be described according to embodiments of the present invention. For example, referring to  FIG. 7 , a dataflow diagram is shown of a system  700  for measuring the vocality of a plurality of users  702  according to one embodiment of the present invention. Referring to  FIGS. 8A-8B , flowcharts are shown of a method  800  performed by the system  700  of  FIG. 7  according to one embodiment of the present invention. 
         [0053]    The users  702  provide textual input  704  to the system  700  ( FIG. 8A , step  802 ). The textual input  704  may take any form, such as free-form text (such as responses to a survey), email messages, web pages, word processing documents, or any combination thereof. The textual input  704  may, for example, be part of the free-text survey responses  108 T shown in  FIG. 1 , in which case the vocality measurement process  800  illustrated in FIGS.  7  and  8 A- 8 C may be integrated with the calibration process  100  illustrated in  FIGS. 1 and 2 , and/or with the emotivity score generation process  600  illustrated in  FIGS. 5 and 6 , such that the same set of user inputs  108  is used to calibrate the system  100  of  FIG. 1 , to generate emotivity scores, and to generate vocality scores. 
         [0054]    Note, however, that the users  702  shown in  FIG. 7  need not have provided any of the survey responses  108  shown in  FIG. 1 . Furthermore, although the vocality scores  712  shown in  FIG. 7  are generated using information in addition to the users&#39; textual input  704 , this is not a requirement of the present invention. Rather, all that is required from the users  702  to identify the users&#39; vocality scores  712  are the users&#39; textual input  704 . 
         [0055]    A word count engine  706  produces various counts  708   a - d  of words in the users&#39; textual input  704 . More specifically, in the particular example illustrated in FIGS.  7  and  8 A- 8 C, the word count engine  706  counts the number of words provided by each of the users  702  in response to one or more questions which prompted the users  702  to describe positive aspects of the users&#39; relationship partners (step  804   a ). For example, consider a survey question such as, “What does your partner do well, that you would like him or her to continue to do?” Such a question solicits positive information about the user&#39;s relationship partner. In step  804   a , the word count engine  706  may count the number of words in the textual input  704  provided by each of the users  702  in response to such a question, to produce what are referred to herein as a count of the “best” words for each of the users  702 . If the survey includes multiple such questions, then each user&#39;s “best count” may be equal to the aggregate number of words provided by the user in response to all such questions. 
         [0056]    Similarly, the word count engine  706  counts the number of words provided by each of the users  702  in response to one or more questions which prompted the users  702  to describe negative aspects of the users&#39; relationship partners (step  804   b ). For example, consider a survey question such as, “What does your partner not do well, that you would like him or her to improve?” Such a question solicits negative information about the user&#39;s relationship partner. In step  804   b , the word count engine  706  may count the number of words in the textual input  704  provided by each of the users  702  in response to such a question, to produce what are referred to herein as a count of the “worst” words for each of the users  702 . If the survey includes multiple such questions, then each user&#39;s “worst count” may be equal to the aggregate number of words provided by the user in response to all such questions. 
         [0057]    Similarly, the word count engine  706  counts the number of words provided by each of the users  702  in response to one or more open-ended questions (step  804   c ). For example, consider a survey question such as, “Is there any other information you would like to provide about your relationship partner?” Such a question solicits open-ended information about the user&#39;s relationship partner. In step  804   c , the word count engine  706  may count the number of words in the textual input  704  provided by each of the users  702  in response to such a question, to produce what are referred to herein as a count of the “open” words for each of the users  702 . If the survey includes multiple such questions, then each user&#39;s “open count” may be equal to the aggregate number of words provided by the user in response to all such questions. 
         [0058]    Input may be identified as being associated with positive, negative, or open-ended information even if such information was not provided in response to survey questions. Rather, any technique may be used to identify input from the users  702  as providing positive, negative, or open-ended information and to count the number of words in such input. For example, the input that is used in steps  804   a - c  above may be drawn from email messages, word processing documents, web pages, or other data created by the users  702 . Furthermore, the word count engine  706  may count any subset of the input provided by the users  702 . For example, if the users&#39; input  704  is a set of survey responses which include both multiple-choice responses and free text responses, the word count engine  506  may be configured only to count words in the user&#39;s free text responses. 
         [0059]    The word count engine  706  sums the positive, negative, and open-ended word counts  708   a - c  to produce net word counts  708   d  for each of the users  702  (step  804   d ). Note that although in the embodiment illustrated in FIGS.  7  and  8 A- 8 C, the net word count  708   d  for a particular user is the sum of three other word counts  708   a - c , this is merely an example and does not constitute a limitation of the present invention. Rather, the net word count  708   d  may be a sum of any number of other word counts. Furthermore, the component word counts need not represent positive, negative, and open-ended information. Rather, each of the component word counts may be defined to correspond to any desired kind of information. 
         [0060]    The users&#39; vocality scores  712  may be derived from the word counts  708   a - d  and, optionally, from loyalty levels  716  produced by the loyalty level engine  306  based on input  714  provided by the users  714  in the manner described above with respect to  FIGS. 3 and 4  (step  806 ). The input  714  that is used to identify the users&#39; loyalty levels  716  may be, but need not be, the same as the input  704  provided to the word count engine  706 . For example, the users  702  may be provided with a set of surveys, the answers to which may be used to derive the users&#39; loyalty levels  716 , emotivity scores  512 , and vocality scores  712 . Alternatively, however, the loyalty levels  716 , emotivity scores  512 , and vocality scores  712  may be derived from separate sets of inputs. For example, the loyalty levels  716  may be derived from answers to surveys, while the emotivity scores  512  and vocality scores  712  may be derived from free-text in email messages, word processing documents, and blog entries. 
         [0061]    A statistics engine  718  generates, for each loyalty level, statistics  720   a - d  derived from the word counts  708   a - d  (step  808 ). In the example shown in  FIG. 7 , the statistics  720   a - d  include, for each loyalty level, the means and standard deviations of the corresponding word counts  708   a - d . For example, statistics  720   a  include means  722   a  and  722   d , derived from the “best” word counts  708   a . Assuming that there are four loyalty levels, means  722   a  include four means: the mean of the best word counts for users with loyalty levels of −1, 1, 2, and 3, respectively. Similarly, standard deviations  722   b  include four standard deviations: the standard deviations of the best word counts for users with loyalty levels of −1, 1, 2, and 3, respectively. 
         [0062]    Similarly, statistics  720   b  include means  726   a  and standard deviations  726   b , derived from “worst” word counts  708   b ; statistics  720   c  include means  730   a  and standard deviations  730   b , derived from “open” word counts  708   c ; and statistics  720   c  include means  734   a  and  734   b , derived from “open” word counts  708   c . The statistics engine  718  also identifies statistics  720   e  (including means  738   a  and standard deviations  738   b ) of the net word counts  708   d  across all users  702  (step  810 ). Note that all of these statistics  720   a - e  are merely examples and do not constitute limitations of the present invention; other statistics may be used to perform the functions described herein as being performed by the statistics  720   a - e.    
         [0063]    Referring to  FIG. 8B , a vocality engine  740  generates vocality scores  712  for the users  702  as follows (step  812 ). For each user (step  814 ), the vocality engine  740  identifies the user&#39;s loyalty level (step  815 ). The method  800  then identifies the user&#39;s vocality score based on the user&#39;s loyalty level (step  816 ). Examples of techniques that may be used to compute the user&#39;s vocality score are described below with respect to  FIGS. 8B and 8C . The vocality scores for the remaining users may be computed by repeating steps  815 - 816  (step  817 ). 
         [0064]    Referring to  FIG. 8B , a flowchart is shown of one method that may be used to identify a user&#39;s vocality score, assuming that the user&#39;s loyalty level is known. If the user&#39;s loyalty level is −1 (step  818 ), then: (1) if the user&#39;s worst word count  708   b  is one standard deviation  726   b  or more above the mean  726   a  for users having a loyalty level of −1 (step  820 ), then a vocality score of “Vocal Detractor” is assigned to the user (step  822 ); (2) otherwise, if the user&#39;s net word count  708   d  is one standard deviation  734   b  or more above the mean  734   a  for users having a loyalty level of −1 (step  824 ), then a vocality score of “Vocal” is assigned to the user (step  826 ); (3) otherwise, a vocality score of “Non-Vocal” is assigned to the user (step  828 ). 
         [0065]    If the user&#39;s loyalty level is 3 (step  830 ), then: (1) if the user&#39;s best word count  708   a  is one standard deviation  728   b  or more above the mean  728   a  for users having a loyalty level of 3 (step  832 ), then a vocality score of “Vocal Advocate” is assigned to the user (step  834 ); (2) otherwise, if the user&#39;s net word count  708   d  is one standard deviation  734   b  or more above the mean  734   a  for users having a loyalty level of 3 (step  836 ), then a vocality score of “Vocal” is assigned to the user (step  838 ); (3) otherwise, a score of “Non-Vocal” is assigned to the user (step  840 ). 
         [0066]    If the user&#39;s loyalty level is 1 or 2 (step  842 ), then: (1) if the user&#39;s net word count  708   d  is one standard deviation  738   b  or more above the mean  738   a  for all users (step  844 ), then a vocality score of “Vocal” is assigned to the user (step  846 ); (2) otherwise, a vocality score of “Non-Vocal” is assigned to the user (step  848 ). 
         [0067]    Once the users&#39; vocality scores  712  have been identified, a vocality rendering engine  742  may produce output  744  which represents the vocality scores  712  in any of a variety of ways (step  850 ). For example, when the profile for a particular user is displayed, the profile may display information such as the user&#39;s name, title, email address, and loyalty level. The display may also include an icon, such as a megaphone, which graphically represents the user&#39;s vocality score. For example, the bullhorn may have zero or more lines emanating from it, where the number, shape, and/or size of the lines corresponds to the user&#39;s vocality score. For example, a “Non-Vocal” user&#39;s megaphone may have no lines emanating from it ( FIG. 9A ), a “Vocal” user&#39;s megaphone may have several lines emanating from it ( FIG. 9B ), a “Vocal Advocate” user&#39;s megaphone may have lines with plus signs emanating from it ( FIG. 9C ), and a “Vocal Detractor” user&#39;s megaphone may have lines with minus signs emanating from it ( FIG. 9D ). Clicking on the megaphone may cause the system  700  to display the user&#39;s textual input  704 , or other data created by the user which resulted in the user&#39;s vocality score. 
         [0068]    The techniques described above with respect to FIGS.  7  and  8 A- 8 C are merely one example of how vocality may be measured, and do not constitute a limitation of the present invention. For example, the distinction in FIGS.  7  and  8 A- 8 C between “Detractors” and “Advocates” may be ignored when measuring users&#39; vocality, so that users are merely labeled “Vocal” or “Non-Vocal” depending on the numbers of words in their input. Such a technique may be applied in  FIG. 8B , for example, by labeling users as “Vocal” in step  822  (instead of “Vocal Detractors”) and in step  834  (instead of “Vocal Advocates”). 
         [0069]    Furthermore, although in the example just described, users are classified either as “Vocal” or “Non-Vocal,” users&#39; degrees of vocality may be divided into more than two categories. Rather, any number of values of any kind may be used to represent users&#39; degrees of vocality. 
         [0070]    Furthermore, in the example illustrated in FIGS.  7  and  8 A- 8 C, any given user is classified as “Vocal” or “Non-Vocal” based on the number of words in that user&#39;s input relative to the numbers of words used by other users in their input. Although using statistical measures of the numbers of words used by a population of users to draw the dividing line between “Vocal” and “Non-Vocal” users may be useful, it is not a requirement of the present invention. Rather, breakpoints between “Vocal” and “Non-Vocal” users (and between any other values used to represent vocality) may be absolute values, chosen in any manner, rather than values chosen relative to the input of a population of users. More generally, such breakpoints may be chosen in any manner and may change over time. 
         [0071]    As yet another example of how vocality may be measured, consider an embodiment of the present invention which uses three vocality values, referred to herein as “Non-Vocal,” “Vocal Advocate,” and “Vocal Detractor.” In this embodiment, whether a particular user is Vocal (whether Advocate or Detractor) rather than Non-Vocal may be determined in the manner described above with respect to FIGS.  7  and  8 A- 8 B, namely by determining whether the number of words used by the user is more than one standard of deviation greater than the mean for users having the same loyalty level. In this embodiment, however, whether the user is considered an Advocate or a Detractor is based not on the user&#39;s loyalty level, but rather on the ratio of the user&#39;s “best” word count to the user&#39;s “worst” word count. 
         [0072]    More specifically, once the user&#39;s loyalty level is known ( FIG. 8A , step  815 ), then the method shown in  FIG. 8C  may be used to compute the user&#39;s vocality score. If the user&#39;s net word count  708   d  is one standard deviation  734   b  or more above the mean  734   a  for users having the same loyalty level (step  862 ), then a vocality score of “Vocal” is assigned to the user (step  864 ). Otherwise, the user is assigned a vocality score of “Non-Vocal” (step  866 ). 
         [0073]    If the user is labeled as Vocal (step  864 ), then the method computes the ratio of the user&#39;s best word count  708   a  to the user&#39;s worst word count  708   b  (step  868 ). The method may add a nominal value, such as 0.1, to the user&#39;s worst word count in step  868  to avoid division by zero. The method then determines whether the ratio is greater than some predetermined threshold, such as 0.66 (step  870 ). If the ratio exceeds the threshold, the user is assigned a vocality score of “Vocal Advocate” (step  872 ). Otherwise, the user is assigned a vocality score of “Vocal Detractor” (step  874 ). 
         [0074]    Note that the method of  FIG. 8C  may result in users with low (e.g., −1) loyalty levels being labeled as Vocal Advocates, and users with high (e.g., 3) loyalty levels being labeled as Vocal Detractors. This is surprising, since one would expect users with low loyalty levels to be detractors and users with high loyalty levels to be advocates. Special attention should be paid to Advocates with low loyalty levels and Detractors with high loyalty levels, because by focusing on these customers, both the Loyalty and Vocality of the customer base can be increased. Alternatively, the loyalty levels of conflicted respondents may be modified so that their loyalty levels match their status as Advocates or Detractors, as indicated by their vocality. More specifically, if a user&#39;s vocality indicates that he or she is an Advocate, then the user&#39;s loyalty level may be incremented or changed to the maximum loyalty level. Conversely, if the user&#39;s vocality indicates that he or she is a Detractor, then the user&#39;s loyalty level may be decremented or changed to the minimum loyalty level. 
         [0075]    The techniques described above are examples of ways in which the emotivity and/or vocality of text may be measured. Although it may be useful to measure the emotivity and/or vocality of a particular text written by a particular user, it may also be useful to measure the emotivity and/or vocality of: (1) a particular user over time, and/or (2) a collection of users over time. Such measurements may be used to identify trends in emotivity and/or vocality, and thereby to identify trends in loyalty, over time. 
         [0076]    For example, referring to  FIG. 10 , a flowchart is shown of a method  1000  performed in one embodiment of the present invention to identify trends in loyalty of a single user over time. The method  1000  receives text input from one or more users (step  1002 ). The method  1000  then identifies a vocality score (step  1004   a ) and/or an emotivity score (step  1004   b ) for each of the users, based on the received text input. The vocality scores may, for example, be identified in the manner disclosed herein in connection with FIGS.  7  and  8 A- 8 C, or in any other way. Similarly, the emotivity scores may, for example, be identified in the manner disclosed herein in connection with  FIGS. 5-6 , or in any other manner. 
         [0077]    Note that the method  1000  may generate only vocality scores, only emotivity scores, or both vocality scores and emotivity scores for each of the users. Furthermore, the emotivity scores are merely one example of measurements that may be generated using latent semantic analysis. Therefore, other forms of latent semantic analysis may be applied to the input text to produce measurements other than emotivity scores. 
         [0078]    The method  1000  may then identify one or more measurements of velocity in: the vocality scores (step  1006   a ), the latent semantic analysis (e.g., emotivity) scores (step  1006   b ), and the combination of the vocality and latent semantic analysis scores (step  1006   c ). In general, the “velocity” of a set of scores over time refers to the rate of change of the scores over time. Such velocity may be measured, for example, using any known techniques for measuring velocity, where the series of scores is treated as a series of positions, each of which is associated with a particular time. For example, if a first score S 1  occurs at time T 1 , and a second score S 2  occurs at time T 2 , the velocity V for this pair of scores may be computed as (S 2 −S 1 )/(T 2 −T 1 ). 
         [0079]    The “time” values associated with scores may be identified in any of a variety of ways. For example, the time value of a particular score may be equal to or derived from the creation time of the user input (e.g., survey answers) from which the score was produced. As another example, each input (e.g., set of survey responses) received from a particular user with respect to a particular relationship partner of that user may be assigned sequential “time” values, e.g., 1, 2, 3, 4, independent of the chronological times at which those inputs were created or received. 
         [0080]    Velocity may, however, be computed in other ways. For example, in some situations one may only be interested in whether a velocity is non-zero. In such a case, any non-zero velocity may be converted to a normalized value, such as 1. As another example, in some situations one may only be interested in whether a velocity is negative, zero, or positive. In such a case, negative velocities may be converted to a normalized value such as −1, zero velocities may remain zero, and positive velocities may be converted to a normalized value such as 1. These are merely examples of ways in which velocities may be computed. 
         [0081]    Furthermore, any derivative of velocity, such as acceleration, may be computed based on the vocality/semantic analysis scores, and/or directly based on velocity or other derivatives of velocity. Therefore, any discussion of “velocity” herein should be understood to refer not only to velocity but also to acceleration and other derivates of velocity. 
         [0082]    The method  1000  may, for example, identify for each user any one or more of the following: (1) a vocality velocity measurement based on the set of vocality scores for that user over time; (2) a latent semantic analysis velocity measurement based on the set of latent semantic analysis (e.g., emotivity) scores for that user over time; and (3) a combined velocity measurement based on both the vocality measurements and the latent semantic analysis measurements for that user over time. The combined velocity measurement may be calculated in any way, such as an average or weighted sum of the user&#39;s vocality velocity measurement and latent semantic analysis velocity measurement. 
         [0083]    As another example, each of the user&#39;s vocality and latent semantic analysis measurements for a particular text may be combined together to produce a combined content measurement. The user&#39;s combined velocity measurement may then be calculated as the velocity of the user&#39;s combined content measurements. 
         [0084]    The velocity measurement(s) associated with a particular user may be used for a variety of purposes. For example, a company or other relationship partner may be interested in knowing the velocities of the company&#39;s customers, so that the company may identify customers in need of attention. 
         [0085]    For example, referring again to  FIG. 10 , the method  1000  may enter a loop over all user&#39;s U (step  1008 ). Assume for purposes of the following example that user U is a company. The method  1000  then enters a loop over each partner P of user U (step  1010 ). Assume for purposes of the following example that the partners P are customers of company U. 
         [0086]    The method  1000  identifies a velocity of customer P with respect to company U (step  1012 ). The velocity identified in step  1012  may, for example, be any of the velocities described above with respect to steps  1006   a - c.    
         [0087]    If the velocity identified in step  1012  exceeds a predetermined threshold (step  1014 ), the method  1000  notifies company U of customer P&#39;s velocity (step  1016 ). Note that the threshold may be applied to the absolute value of the velocity, so that the company is notified of both large positive and large negative velocities. Furthermore, a function other than a simple threshold may be applied to the velocity to determine whether to notify the company of the velocity. 
         [0088]    Alternatively, for example, the method  1000  may notify company U of the velocity of all customers, not just those customers whose velocities exceed a predetermined value. Furthermore, the notification performed in step  1016  may take any form. For example, it may include the value of customer P&#39;s velocity, or simply be a warning to company U that customer P requires attention. 
         [0089]    Furthermore, the method  1000  may take into account the value of customer P&#39;s velocity when deciding which kind of notification to provide and/or which kind of action to take. For example, a customer whose vocality has a very high velocity may require more immediate attention than a customer with a lower velocity. The method  1000  may take this into account by prioritizing the customers P according to their velocities or functions thereof. For example, the method  1000  may instruct company U to attend to customer P within an amount of time that is derived from customer P&#39;s velocity, where larger velocities result in shorter amounts of time. 
         [0090]    The method  1000  may repeat the process described above for the remaining customers of company U (step  1018 ) and for other companies (step  1020 ). 
         [0091]    The techniques disclosed herein for measuring emotivity and vocality have a variety of advantages. For example, purely quantitative survey responses—such as “Rate your business partner on responsiveness on a scale of 1 through 5”—provide only limited information about the “loyalty climate” that characterizes the relationship between the survey respondent and the subject of the survey. A customer who responds with a value of 5 (highly satisfied) may still have little or no emotional attachment to his business partner. In fact, a respondent who responds with a value of 4 may feel more strongly about the business partner than someone who responds with a value of 5, yet such purely numerical responses fail to capture such subtle but important differences in loyalty climate. 
         [0092]    The techniques disclosed herein can fill this gap in understanding by providing meaningful quantitative measures of respondents&#39; feelings about their relationship partners, in the form of emotivity and vocality measurements. These measurements may be based on free text input provided by the respondents, and may therefore capture information which would otherwise go unrecognized solely by analyzing the respondents&#39; numerical survey answers. 
         [0093]    Although emotivity and vocality both measure intensity of feeling in some sense, they provide such measures in different ways that complement each other. Emotivity is based on semantic analysis of the words used by respondents, and therefore can capture very strong feelings expressed even in very few words. Vocality is based on the number of words used by respondents, and therefore can recognize strong feelings in respondents&#39; responsive even when the individual words used by the respondents do not clearly indicate such feelings. Vocality is particularly useful as a complement to emotivity in light of the difficulty of performing semantic analysis of natural languages both automatically and accurately. 
         [0094]    Identifying how strongly one partner to a relationship feels about the other partner is important because identification of such strength of feeling can be used as part of an overall process of identifying the loyalty of the first partner to the second partner. For example, the above-referenced patent application entitled, “Loyalty Measurement” discloses a system for calculating a loyalty index for a user based on a plurality of loyalty dimensions. Emotivity and vocality may be added as additional dimensions within such a system. Once emotivity scores are calculated for a population of users, the emotivity scores may be normalized to fit within the same range as scores in the other dimensions. The emotivity scores may then be weighted by a regression coefficient in the same manner as the other dimensions. Vocality may be integrated within the system in the same manner. Use of emotivity and/or vocality in this way may provide all of the benefits described in the “Loyalty Measurement” patent application. 
         [0095]    One potential drawback of attempting to identify a person&#39;s strength of feeling, whether measured in terms of emotivity or vocality, based on textual input is that providing such textual input can be time-consuming. As a result, it may be difficult to obtain cooperation from users in providing such input. One advantage of embodiments of the present invention in this respect is that the textual input that is used to identify a user&#39;s emotivity and vocality scores may take the form of email messages, word processing documents, web pages, blogs, text messages, comment forms, transcribed phone conversations (such as customer service calls) and voicemail messages, and other text that the user has already written for other purposes. In other words, a user&#39;s emotivity and vocality scores may be calculated without requiring the user to write any additional text specifically for use in the emotivity and vocality measurements. The ability to calculate a user&#39;s emotivity and vocality scores based on existing documents also expands the amount of textual input that may be used to calculate such scores and thereby increases the accuracy of those scores. 
         [0096]    Another benefit of certain embodiments of the present invention is that the “Emo list”  122   a , which includes words representing strong emotions, is not selected arbitrarily or by reference to any predetermined source of words (such as a dictionary or thesaurus), but rather is selected by identifying words used by users having very high and very low loyalty levels. As a result, the Emo list may contain words which reflect strong emotions, but which may not have dictionary definitions representing strong emotions, or which would not otherwise have been identified as “strong emotion” words. Since a word will be included on the Emo list if that word is used by high-loyalty and low-loyalty users, but not by middle-loyalty users, the Emo list is likely to include the words used by passionate users within a particular community. As a result, calculating each user&#39;s emotivity score based at least in part on the contents of the Emo list enables the emotivity score to reflect more accurately how emotive each user is in relation to other members of the community, not just in relation to predetermined (and possibly incorrect) notions of which words reflect strong emotions. 
         [0097]    At the same time, the use of both the Emo list—which is generated based on words used within the community—and the positive and negative lists—which are generated based on dictionary definitions of words—ensures that the emotivity score is not unduly influenced either by unusual usages of words within the community or by predetermined notions of which words reflect strong emotions. Furthermore, the relative influence of the words in the Emo list  122   a , positive list  122   b , and negative list  122   c  need not be determined arbitrarily. Rather, the techniques disclosed above with respect to  FIGS. 1 and 2  may be used to assign weights A, B, and C in Equation 1 based on input provided by users  106 . In this way, the weights A, B, and C may be chosen to reflect the actual relationship between strong-emotion words, positive-emotion words, and negative-emotion words, respectively, on loyalty. For example, based on one set of data we have identified the values of 10, 1, and −15 for coefficients A, B, and C, respectively, reflecting the strong relationship between the use of negative words on loyalty. Calibrating the emotivity coefficients based on statistical analysis of empirical data in this way enables the emotivity scores generated by embodiments of the present invention to more accurately reflect users&#39; emotivity. 
         [0098]    Another benefit of certain embodiments of the present invention is that they may be used to identify the velocity of user&#39;s vocalities. It may be useful to identify such velocities because the raw vocality of a user, while helpful, may provide limited information about the user. For example, if a particular user&#39;s baseline vocality is relatively high and then begins to drop over time to lower and lower values, this downward trend may be evidence that the user&#39;s loyalty is decreasing, or that the user is otherwise in need of attention. The user&#39;s new, lower, vocality scores, however, may still be relatively high compared to the vocality scores of other users or compared to some other baseline value. Merely analyzing individual vocality scores of the user, therefore, may fail to indicate that the user is in need of attention. In contrast, analyzing a sequence of the user&#39;s vocality scores over time and identifying the velocity of such scores may enable the system to draw conclusions and take actions, regardless of the absolute values of such scores. 
         [0099]    It is to be understood that although the invention has been described above in terms of particular embodiments, the foregoing embodiments are provided as illustrative only, and do not limit or define the scope of the invention. Various other embodiments, including but not limited to the following, are also within the scope of the claims. For example, elements and components described herein may be further divided into additional components or joined together to form fewer components for performing the same functions. 
         [0100]    Although in certain embodiments disclosed herein, both emotivity and vocality are represented as single numbers (such as the emotivity score  512  n  FIG. 5  and the vocality score  712  in  FIG. 7 ), this is not a limitation of the present invention. Rather, emotivity and vocality may be represented in other ways, such as by multiple values. 
         [0101]    Although particular techniques are disclosed herein for generating the Emo list  122   a , positive list  122   b , and negative list  122   c , such lists may be generated in other ways. Furthermore, such lists  122   a - c  may be updated over time. For example, the Emo list  122   a  may be updated as additional free-text responses are received from users who loyalty levels are already known. For example, if text is received from a person having a loyalty level of −1 or 3, then any words used by that person may be added to the Emo list  122   a , so long as those words are not in the “non-emotive” list  320   b . As another example, the system  100  may scan emails within a corporation and continuously update the Emo list  122   a  based on words within emails sent by senders whose loyalty levels are already known. 
         [0102]    Although in the example illustrated in FIGS.  7  and  8 A- 8 C, the statistics  720   a - 720   e  are means and standard deviations, other statistics may be used in the process of measuring vocality. For example, other kinds of averages, such as modes or medians, may be used. Furthermore, in  FIGS. 8A-8C , a single standard deviation serves as the breakpoint between different vocality levels. This is merely one example, however, and does not constitute a limitation of the present invention. Any breakpoint(s) between different vocality levels may be used. 
         [0103]    The techniques described above may be implemented, for example, in hardware, software tangibly embodied in a computer-readable medium, firmware, or any combination thereof. The techniques described above may be implemented in one or more computer programs executing on a programmable computer including a processor, a storage medium readable by the processor (including, for example, volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Program code may be applied to input entered using the input device to perform the functions described and to generate output. The output may be provided to one or more output devices. 
         [0104]    Each computer program within the scope of the claims below may be implemented in any programming language, such as assembly language, machine language, a high-level procedural programming language, or an object-oriented programming language. The programming language may, for example, be a compiled or interpreted programming language. 
         [0105]    Each such computer program may be implemented in a computer program product tangibly embodied in a machine-readable storage device for execution by a computer processor. Method steps of the invention may be performed by a computer processor executing a program tangibly embodied on a computer-readable medium to perform functions of the invention by operating on input and generating output. Suitable processors include, by way of example, both general and special purpose microprocessors. Generally, the processor receives instructions and data from a read-only memory and/or a random access memory. Storage devices suitable for tangibly embodying computer program instructions include, for example, all forms of non-volatile memory, such as semiconductor memory devices, including EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROMs. Any of the foregoing may be supplemented by, or incorporated in, specially-designed ASICs (application-specific integrated circuits) or FPGAs (Field-Programmable Gate Arrays). A computer can generally also receive programs and data from a storage medium such as an internal disk (not shown) or a removable disk. These elements will also be found in a conventional desktop or workstation computer as well as other computers suitable for executing computer programs implementing the methods described herein, which may be used in conjunction with any digital print engine or marking engine, display monitor, or other raster output device capable of producing color or gray scale pixels on paper, film, display screen, or other output medium.

Technology Classification (CPC): 6