Patent Publication Number: US-2016232232-A1

Title: Mining product aspects from opinion text

Description:
FIELD OF THE INVENTION 
     The present disclosure generally relates to automated textual analysis, and more particularly to determining textually expressed sentiments. 
     BACKGROUND 
     Unstructured digital data such as unstructured digital text data often contains useful information. This information may be latent and therefore not readily accessible to a user. For example, opinions expressed in online reviews for a product may help a potential buyer decide whether to purchase that product. The product may be associated with thousands of reviews, and reading more than a handful of the reviews may be impractical for the buyer. In another example, a discussion thread in an online forum may contain hundreds or thousands of posts. It may be impractical for a single forum user to review each post. The result may be that potentially valuable the information in such reviews or posts goes unused. 
     BRIEF SUMMARY 
     Embodiments of the present disclosure provide a method, system, and computer program product for extracting an aspect from a text stream. A text stream having one or more sentences is received, and any number of the one or more sentences are parsed to determine corresponding subject-verb-object (SVO) triples. Each sentence whose corresponding SVO triple contains an identified verb is selected, based on the identified verb, or a lemma of the identified verb, matching a predefined verb. A subject of each selected sentence is identified as an aspect candidate. Each identified aspect candidate is tokenized and normalized. One or more n-grams are generated for each tokenized and normalized aspect candidate. For each generated n-gram, a frequency at which the n-gram is generated is determined. A number of the generated n-grams are selected as aspects based on the frequency with which the number of n-grams are generated. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         FIG. 1  is a block diagram of a computer system for extracting aspects from text, according to an embodiment of the present disclosure. 
         FIG. 2  is a block diagram depicting details of an opinion analysis module of the computer system of  FIG. 1 , according to an embodiment of the present disclosure. 
         FIG. 3  is a flowchart of a method for extracting aspects from text, according to an embodiment of the present disclosure. 
         FIG. 4  is a block diagram of a computing device, according to an embodiment of the present disclosure. 
         FIG. 5  is a schematic block diagram of an illustrative cloud computing environment, according to an embodiment of the present disclosure. 
         FIG. 6  is a multi-layered functional illustration of the cloud computing environment depicted in  FIG. 5 , according to an embodiment of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Generally, embodiments of the present disclosure provide for extraction of Aspects from text. According to one embodiment, an Aspect may correspond to an n-gram generated using a subject of the text. 
       FIG. 1  is a block diagram of an Aspect Extraction System  100  for extracting one or more Aspects from text, according to an embodiment of the present disclosure. The Aspect Extraction System  100  may be a computing device having a tangible Storage Device  130  and a Program  102  for execution by a processor (not shown). The Program  102  may include a Data Storage Module  110  that loads and stores one or more of the following data types from/to the Storage Device  130  (or from/to another tangible storage device): Text Collection(s)  112 , Lexicon(s)  114 , and Database(s)  116 . The Program  102  may also include an Opinion Analysis Module  120  which may communicate with the Data Storage Module  110  to obtain data to analyze, and to provide analysis results that may be stored on the tangible Storage Device  130 . The Opinion Analysis Module  120  may perform a variety of analysis functions including, for example, extracting Aspects from text loaded by the Data Storage Module  110 . 
     The Aspect Extraction System  100  may be a computing device such as the device described in connection with  FIG. 4 , below. For example, the Aspect Extraction System  100  may be a server, laptop, desktop, or mobile device. Each such device may include, for example, a processor, one or more tangible storage devices, one or more programs stored on the one or more tangible storage devices, or another tangible storage device, for execution by the processor, one or more I/O devices, networking components, and other components and/or peripherals. The Aspect Extraction System  100  may be operatively connected to one or more other systems or devices via, for example, a communications network using, for example, a LAN or WAN protocol. 
     The Text Collection  112  data type loaded/stored by the Data Storage Module  110  from/onto the Storage Device  130  and used by the Opinion Analysis Module  120  may include a collection of text streams. A text stream may include one or more of: characters, words, phrases, clauses, sentences, paragraphs, or any other grouping of text data. Text streams may be in structured or unstructured format. Structured text streams may have a predefined or predetermined significance (e.g., a set of text elements may be identified as forming part of a sentence having identified boundaries). Unstructured text streams may include the same text stream as structured text, without information as to the text stream&#39;s definition, structure, or significance. Structured text streams may include, without limitation, online product reviews, posts on online discussion forums; social media posts; digital text documents; online advertisements or testimonials; or a combination thereof. 
     According to an embodiment of the present disclosure, the Text Collection  112  may include text streams that can be classified as opinion text. Opinion text may include text streams that specify or relate to a subject of the opinion text, and individually or collectively convey a sentiment towards that subject. An example of opinion text may be the sentence S 1 : “The display is made from highly damage-resistant glass.” In this example, a subject of the sentence may be “the display”, and “highly damage resistant” may be an opinion that is directly or indirectly expressed towards that subject. Whether a given text stream may be classified as opinion text may be determined by the System&#39;s  100  Opinion Analysis Module  120  using the given text stream, and/or related text streams, in conjunction with the Lexicons  114  and other configurations of the Aspect Extraction System  100 . For example, certain words or phrases, such as “highly damage-resistant”, may be predefined as being indicative of sentiment. 
     According to an exemplary embodiment of the present disclosure, the Text Collection  112  may be a collection of product (or service) reviews. A product review may include a text stream that is associated with a product (which may be offered for sale), wherein the product may be the subject of the product review. The product review may include opinion text that expresses an opinion towards the product. Continuing with the above example of S 1 , a product review may be: “The display is made from highly damage-resistant glass.” In this example, the “display” may be the subject/product, and “highly damage-resistant” may be an opinion expressed towards the product. Another product review relating to the display may include opinion text regarding the display&#39;s price. For example, another product review may be, “The display is expensive.”. Alternatively, both of these sentences may be part of a single, multi-sentence product review. 
     The Opinion Analysis Module  120  may analyze text, or text streams, received from the Text Collection  112  by using, in part, the Lexicons  114 . The Lexicons  114  may include predefined characters, words, phrases, clauses, sentences, paragraphs, or any other grouping of text that are defined to have a meaning and/or significance. For example, a word in the Lexicon  114  may have a meaning, and a grammatical role or significance (for example, the word may be a subject, object, verb, punctuation, etc.). The word&#39;s significance may be relative to other words or determined based on context, such that a word may have a different significance and/or grammatical role depending on the context in which it is used. In one embodiment, depending on its context, for example, a given word or phrase may be defined to convey sentiment, whereas it may convey no such sentiment in another context. 
     For example, in the case of S 1 , “The display is made from highly damage-resistant glass.”, the Opinion Analysis Module  120  may analyze each element of the sentence using the Lexicons  114  to determine, for each element, its meaning, whether it is part of a broader text element (for example, whether it is a word or phrase in a sentence), and its significance, such as its grammatical role. 
     The Database Module  116  may store the data that the Opinion Analysis Module  120  generates, and may load such data for future use. Such data may include, for example, comparison results, extracted subjects, tokenization and normalization data, generated n-grams, frequency analysis results, and aspect selection results, and other information, as will be discussed in greater detail with respect to  FIG. 2 , below. 
       FIG. 2  is a block diagram depicting component modules of the Opinion Analysis Module  120  of the Aspect Extraction System  100  depicted in  FIG. 1 , according to an embodiment of the present disclosure. The Opinion Analysis Module  120  may include one or more of the following modules: a Snippet Extraction Module  122  and an Aspect Extraction Module  124 . Generally, the Snippet Extraction Module  122  identifies a subset or “snippet” of a text stream, such as portions of an online product review, that may yield useful information upon further processing. The Aspect Extraction Module  124  may generally process the snippet to extract Aspect candidates from the snippet, and to extract or select one or more Aspects based on the extracted Aspect candidates. 
     The Snippet Extraction Module  122  may include one or more of the following modules: a Sentence Identifier Module  122 A, a Parser Module  122 B, and a SVO Analysis Module  122 C. Through the use of these modules, the Snippet Extraction Module  122  may receive, identify, and/or analyze text streams contained in the Text Collection  112  and loaded by the Data Storage Module  110 . The results of the analysis performed by the Snippet Extraction Module  122  may be used by other modules of the Opinion Analysis Module  120 , such as the Aspect Extraction Module  124 , to perform additional analysis of the Text Collection  112 . 
     According to an embodiment of the present disclosure, the Sentence Identifier Module  122 A may receive text streams in the Text Collection  112  from the Data Storage Module  110 , and identify each sentence therein. Each text stream may include a single sentence or a set of sentences (sentences need not be grammatically complete or correct). The Sentence Identifier Module  122 A may identify each sentence S in each text stream that it receives. It may do so by, for example, identifying sentence boundaries using punctuation marks that delineate sentence boundaries, such as those in the set {. ! ? ; :}. 
     For example, a product review R 1  may include the following text stream: “The display is made from highly damage-resistant glass. The display is expensive. It takes one hour for the device to fully charge.” In this example, the Sentence Identifier Module  122 A may identify three sentences in R 1 :S 1 , comprising “The display is made from highly damage-resistant glass.”; S 2 , comprising “The display is expensive.”; and S 3 , comprising “It takes one hour for the device to fully charge.”. 
     The Parser Module  122 B may parse each sentence S identified by the Sentence Identifier Module  122 A for each text stream (for example, for each product review R) that it receives. Parsing may include identifying the grammatical structure of a sentence and its constituent elements using, for example, the Lexicons  114 . The results may be organized as a parse tree. For example, the Parser Module  122 B may generate a dependency parse, a typed dependency parse, a phrase structure parse, etc. Each parsed sentence S may have a corresponding parse tree T. In one embodiment, the Parser Module  122 B may include the Stanford Parser and may generate its results using the Stanford Dependencies representation. In another embodiment, the Parser Module  122 B may include the Apache OpenNLP™ parser. 
     In the case of the sentence S 1  in the above example, S 1  may have a corresponding parse tree T 1 , based on an embodiment that uses the Stanford Parser, which may be as in TABLE 1, below: 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Example Parse Tree T 1  for the Sentence S 1 : 
               
               
                 “The display is made from highly damage-resistant glass.” 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                   
                 (ROOT 
               
               
                   
                  (S 
               
               
                   
                  (NP (DT The) (NN display)) 
               
               
                   
                  (VP (VBZ is) 
               
               
                   
                   (VP (VBN made) 
               
               
                   
                   (PP (IN from) 
               
               
                   
                    (NP 
               
               
                   
                    (ADJP (RB highly) (JJ damage-resistant)) 
               
               
                   
                    (NN glass))))))) 
               
               
                   
                   
               
            
           
         
       
     
     The SVO Analysis Module  122 C may analyze each parse tree T (corresponding to a sentence S) to identify subject-verb-object (SVO) triples within S, as determined by the Parser Module  122 B. In determining SVO triples, the SVO Analysis Module  122 C may use one or more lemmas of the identified subject, verb, or object of S. 
     A lemma may be defined, in one embodiment of the present disclosure, as a canonical form, dictionary form, or citation form of a set of words that do not necessarily share a stem with such form or with each other. Lemmatization may refer to a process whereby different inflected forms of a word are grouped together so they can be analyzed as a single item. Lemmatization may also refer to the process of determining a lemma for a given text element, such as a word. For example, the word “go” is an irregular verb having conjugated forms that do not share any stem with the word “go”. The word “go” may be defined as a lemma for all conjugated forms of the verb “to go”, such as those in the set {go, goes, went, gone}. 
     For example, in the case of S 1  in TABLE 1, above, the SVO Analysis Module  122 C may determine, based on T 1 , that S 1  includes the subject “the display”, the verb “is made”, and the object “glass”. The SVO Analysis Module  122 C may normalize or lemmatize the verb “is made” to “make”, where “make” may be a predefined lemma for “made”. Accordingly, the corresponding SVO triple of S 1  may be: &lt;the display, make, glass&gt;. Alternatively, the object may be “damage resistant-glass” or “highly damage-resistant glass.” 
     The SVO triples generated by the SVO Analysis Module  122 C of the Snippet Extraction Module  122  may be used by modules of the Aspect Extraction Module  124  to extract Aspects of one or more sentences S. 
     The Aspect Extraction Module  124  may extract Aspects of one or more sentences S based, in part, on analysis of the SVO triples generated by the SVO Analysis Module  122 C. As described above, according to one embodiment, an Aspect may correspond to an n-gram generated using a subject of a given text, such as the sentence S. The Aspect Extraction Module  124  may extract Aspects of S using one or more of the following modules: a Comparison Module  124 A, a Subject Extraction Module  124 B, a Tokenization &amp; Normalization Module  124 C, an n-gram Generation Module  124 D, a Frequency Analysis Module  124 E, and a Aspect Selection Module  124 F. 
     For each sentence S analyzed by the SVO Analysis Module  122 C, the Comparison Module  124 A may compare the sentence&#39;s verb identified in its corresponding SVO triple to verbs stored in a list of predefined verbs. Any sentence S whose verb, as identified in its SVO triple, matches a predefined verb, may be selected by the Opinion Analysis Module  120  for further processing and provided to the Subject Extraction Module  124 B. The list of predefined verbs may be stored in the Database  116 , and may include one or more verbs that are of interest in a particular embodiment of the present disclosure. Where a particular embodiment is used to perform sentiment analysis, for example, a verb of interest may be selected based on how well it is believed to correlate to statements expressing a relevant opinion towards the verb&#39;s subject when used in a sentence. 
     For example, in the case of an embodiment where the Text Collection  112  is a collection of product reviews, the list of predefined verbs may include one or more of the following verbs and their lemmas: {make, cost, last, weigh, measure}. These verbs may be included in the list of predefined verbs because they may be indicative of information that is relevant to characteristics of interest of a product review. For example, when used in a sentence of a product review, these verbs may indicate the product&#39;s characteristics such as its material, cost, durability, weight, and dimensions, respectively. Other verbs that identify other characteristics, such as the product&#39;s color, may not be of interest and may be omitted from the list because it may be, for example, that the product is used as an internal component of another product and is typically not visible. 
     In the case of S 1 , above, the verb “make” (which is the normalized/lemmatized form of “made”) of the SVO triple &lt;the display, make, glass&gt;, may be on the predefined list (alternatively, both “make” and “made” may be the list). Accordingly, the Comparison Module  124 A may select S 1  as a sentence whose corresponding SVO triple contains an identified verb, where the verb and/or its lemma matches a predefined verb. The Comparison Module  124 A may provide the selected S 1  to the Subject Extraction Module  124 B for further processing. 
     The Subject Extraction Module  124 B may process any sentence S whose verb, or a lemmatized form thereof, contained in a corresponding SVO triple, matches a predefined verb as determined by the Comparison Module  124 A. The Subject Extraction Module  124 B may identify the subject of the sentence S, contained in the corresponding SVO triple, as an Aspect candidate C. Accordingly, in an exemplary embodiment, an Aspect candidate C may be defined as a subject within an SVO triple that relates to a verb of interest, where the verb is defined as a verb of interest in a predefined list. For example, in the case of S 1 , above, the Subject Extraction Module  124 B may identify “the display” as an Aspect candidate C 1 , where “the display” is the subject in S 1 &#39;s SVO triple &lt;the display, make, glass&gt;, and where “make” (which is a normalized/lemmatized form of “made”) is included in the list of predefined verbs. 
     The Tokenization &amp; Normalization Module  124 C tokenizes and normalizes each Aspect candidate C identified by the Subject Extraction Module  124 B. Tokenization and normalization of an Aspect candidate may be useful because, for example, they enable the Aspect Extraction Module  124  to generate corresponding n-grams in the n-gram Generation Module  124 D. Tokenization and normalization may be performed using a linguistics tool having a set of definitions and rules for tokenizing and normalizing. In one embodiment of the present disclosure, the tool and/or its definitions may be stored as part of the Aspect Extraction System  100  ( FIG. 1 ). 
     Tokenization may include breaking a text stream (e.g., an Aspect candidate) into constituent elements or tokens, such as symbols, words, phrases, clauses, or other meaningful elements, and/or generating symbols, abbreviations, or acronyms based on such tokens. For example, the string “a b” may be tokenized into {a, [space], b}. As a further example, the string “(C)”, may be tokenized as the symbol “©”. 
     Normalization may include identifying, for a given text element such as a word, a common element that relates the word to one or more other words (or other text elements, such as a symbol, abbreviation, or acronym). Normalization may include, for example, stemming and/or lemmatization. Stemming generally refers to a process whereby a trailing segment of a word is removed, with the word&#39;s “stem” remaining. For example, stemming the verb “marketing” may yield one or more of the following stems, among others: {market, marke, mark, ma, m}. Some of these stems may be useful in relating the verb “marketing” to other verbs or words such as “marketed” or “marketers”, each of which may have one or more stems that match one or more stems of the verb “marketing”. 
     Lemmatization may refer to a process whereby different inflected forms of a word are grouped together so they can be analyzed as a single item. Lemmatization may also refer to the process of determining a lemma for a given text element, such as a word. As described above, a lemma may be defined, in one embodiment of the present disclosure, as a canonical form, dictionary form, or citation form of a set of words. Such words may, but need not, share a stem with the lemma or with each other. For example, the word “go” is an irregular verb having conjugated forms that do not share any stem with the word “go”. The word “go” may be defined as a lemma for all conjugated forms of the verb “to go”, such as those in the set {go, goes, went, gone}. 
     According to an exemplary embodiment of the present disclosure, the tokenization and normalization functions of the Tokenization &amp; Normalization Module  124 C may be performed using the Stanford Natural Language Processing Package. 
     In the case of S 1 , above, the Tokenization &amp; Normalization Module  124 C may tokenize the Aspect candidate C 1 , “the display” (identified as an Aspect candidate by the Subject Extraction Module  124 B), to yield the set of tokens {t, h, e, [space], d, i, s, p, l, a, y}. Based on the tokenization, the Tokenization &amp; Normalization Module  124 C may normalize C 1 . In this case, the normalized form of “the display” may be “display”. In a related example, where the normalization includes lemmatization, “the display” may be lemmatized to yield “monitor” or “screen”, or another word, phrase, or other text element that is defined as the lemma for “display”. 
     The n-gram Generation Module  124 D may receive the output of the Tokenization &amp; Normalization Module  124 C (for example, a tokenized and normalized Aspect candidate C) and generate one or more corresponding n-grams. An n-gram may refer to a contiguous sequence of n items from a given sequence or stream of text. According to an embodiment of the present disclosure, the n-gram Generation Module  124  may generate n-grams of size 1, 2, and 3 for any Aspect candidate C that the module receives. In the case of C 1 , “display”, which may be an Aspect candidate of S 1 , above, the corresponding n-grams may be: {d, i, s, p, l, a, y} for n=1, {di, is, sp, pl, la, ay} for n=2, and {dis, isp, spl, pla, lay} for n=3. 
     The Frequency Analysis Module  124 E may receive the n-grams generated by the n-gram Generation Module  124 D and count how many times each n-gram appears amongst the n-grams for a set of Aspect candidates C whose n-grams have been generated be the n-gram Generation Module  124 D. For example, the n-gram Generation Module  124 D may generate 1,000 n-grams based on tens or hundreds of Aspect candidates selected from amongst the subjects of SVO triples corresponding to sentences of a collection of product reviews R. In this example, it may be that the n-grams {dis, spl, pla} each appear over 100 times, and that other n-grams appear with various lower frequencies. 
     According to an embodiment of the present disclosure, the Aspect Selection Module  124 F may select the top K most frequently appearing n-grams as Aspects (the frequency may be determined by the Frequency Analysis Module  124 E). The value of K may be configurable, including configurable by a user. For example, where K is 3, the Aspect Selection Module  124 F may select the top 3 most frequently appearing n-grams. In the example above, these may be the n-grams {dis, spl, pla}. The Aspect Selection Module  124 F may select these n-grams as Aspects. As part of the selection, the Aspect Selection Module  124 F may apply a filtering criteria to the candidate n-grams before selecting them as Aspects. For example, the criteria may include making n-grams related a particular set of subjects ineligible for selection. 
     In a related embodiment of the present disclosure, the Aspect Selection Module  124 F may base its selection of n-grams as Aspects on one or more n-grams appearing with a frequency greater than a threshold value. For example, the Aspect Selection Module  124 F may select those n-grams that appear more than K times (K may be, for example, 1000). 
     The Aspects selected by the Aspect Selection Module  124 F may also be referred to as “Extracted Aspects.” Extracted Aspects may be used in a variety of ways, including, without limitation, as the raw data used in an automated visual summary of online product reviews. For example, each Extracted Aspect may be provided to a user on a display terminal as a displayed word or another visual element (e.g., a graphic), along with associated elements such as key phrases, review snippets associated with that aspect, and other related information. An Extracted Aspect and/or its associated elements may be color coded in such a visual summary to indicate a sentiment towards the Extracted Aspect and/or its associated elements. For example, where an Extracted Aspect is “display”, referring to a display product, and the display has received more than 50% negative online reviews, the Extracted Aspect “display” may be represented using red font to indicate to a reader of the visual summary to quickly ascertain the majority sentiment expressed towards the “display”. 
       FIG. 3  is a flowchart of a method  300  for extracting aspects from text, according to an embodiment of the present disclosure. The method  300  may be, for example, a method implemented by the Program  102  of the Aspect Extraction System  100  ( FIG. 1 ), and may use the Program&#39;s  102  Data Storage Module  110  and the Opinion Analysis Module  120 . 
     The method  300  may receive, in step  310 , a text stream from a text source, such as the Storage Device  130  ( FIG. 1 ), via the Data Storage Module  110 . The text stream may include characters, words, phrases, clauses, sentences, paragraphs, or any other grouping of text data. In one embodiment, the text stream may include the Text Collection  112  ( FIG. 1 ), which may include a collection of product reviews. Each product review may include one or more sentences associated with a product. 
     The method  300  may extract snippets, in step  322 , from the text collection the method receives in step  310 . A text snippet may refer to a discrete portion of the text stream. A text snippet may include, for example, a sentence, a paragraph, a phrase, etc. In the example where the text stream is a set of product reviews, each snippet may correspond to a single review. Snippets may be extracted at multiple boundaries. For example, each product review may be a snippet, and may itself include snippets corresponding to sentences within the product review. 
     According to an embodiment of the disclosure, the snippet extraction function of step  322  may be facilitated, in part, by identifying sentences in the text stream that the method receives, in step  322 A. Text boundaries may be determined using, for example, a language processing tool, such as the Stanford Parser and its variants (e.g., Stanford CoreNLP). For example, the method  300  may receive the following text stream and identify three corresponding sentences S 1 , S 2 , and S 3 : “The display is made from highly damage-resistant glass. The display is expensive. It takes one hour for the device to fully charge.”, wherein each sentence is followed by a period. 
     The method  300  may parse, in step  322 B, sentences identified in step  322 A. Parsing may be performed using, for example, the Stanford Parser. The parsing may generate a parse tree for each sentence that identifies the sentence&#39;s structure, constituent elements (e.g., words, punctuation marks, etc.), and each constituent element&#39;s significance (e.g., grammatical role). 
     The method  300  may perform a subject-verb-object (SVO) analysis, in step  322 C, for each sentence parsed in step  322 B. The SVO analysis may include determining, based on the parse tree for a given sentence, the sentence&#39;s subject, verb, and object, to generate a corresponding SVO triple. For example, the sentence S 1 , “The display is made of highly damage-resistant glass.” may have the following SVO triple: &lt;the display, make, glass&gt;. Note that the verb in the SVO triple of a sentence may be a normalized and/or lemmatized form of the actual form of the verb used in the sentence. In this example, “make” may be the normalized/lemmatized form of “made”. 
     In step  324 , the method  300  may extract an Aspect from the text received in step  310 , based on snippets of text extracted in step  322  (including steps  322 A-C). According to an exemplary embodiment, the method  300  may perform the Aspect extraction via a set of one or more sub-steps, as follows. 
     According to an embodiment of the disclosure, for each identified and parsed sentence having an SVO triple, the method  300  may compare, in step  324 A, the verb of the SVO triple to a predefined list of verbs to determine whether there is a match. If a match is identified, the corresponding sentence may be selected for further processing by the method  300 . Otherwise, it may be ignored. 
     The predefined list may be a compilation of verbs of interest. Whether a verb is of interest depends on the embodiment of the disclosure and the purpose for which the method  300  is used. For example, where the method  300  is used in an embodiment to aid in performing sentiment analysis of product reviews, the predefined verbs may include those verbs which may be indicative of sentiment expressed towards a characteristic of products. For example, the verb “make” or its variants (e.g., “made”) may be indicative of a product&#39;s constituent material. It may be desirable to ascertain sentiments towards this characteristic of the product. In this example, the verb “make” may be included in the predefined list. In the case of S 1 , for example, the method  300  may determine, in step  324 A, that there is a match. 
     The method  300  may extract, in step  324 B, the subject of any sentence whose SVO contains a verb matching a verb in the predefined list of verbs, as determined in step  324 A, above. The extracted subject may be identified, in step  324 B, as an Aspect candidate. For example, the method  300  may identify “the display”, which is the subject in the SVO of S 1 , above, as an Aspect candidate. 
     In step  324 C, the method  300  may tokenize and lemmatize the Aspect candidate identified in step  324 A. Tokenization of the aspect candidate may include, for example, identifying its constituent parts. For example, the Aspect candidate “the display” may be tokenized as {t, h, e, [space], d, i, s, p, l, a, y}. According to an exemplary embodiment of the disclosure, normalization of the aspect candidate may include lemmatization and/or stemming. For example, “the display” may be normalized/lemmatized to “display”. 
     In step  324 D, the method  300  may generate one or more n-grams of the Aspect candidate identified in step  324 B and tokenized and normalized in step  324 C. According to an embodiment of the disclosure, these may be n-grams of size 1, 2, and/or 3. For example, “the display” may have the following n-grams: {d, i, s, p, l, a, y} for n=1, {di, is, sp, pl, la, ay} for n=2, and {dis, isp, spl, pla, lay} for n=3. 
     In step  324 E, the method  300  may analyze the frequency with which each generated n-gram appears amongst the n-grams generated for all sentences of the received text stream whose sentences have included an SVO triple yielding an Aspect candidate. For example, the method  300  may determine that the n-grams {dis, spl, pla} each appear over 100 times, and that other n-grams appear with various lower frequencies. 
     In step  324 F, the method  300  may select a set of the generated n-grams as Aspects. An Aspect selected by the method  300  may be said to have been “extracted”. According to an exemplary embodiment of the disclosure, selection of Aspects from amongst n-grams of Aspect candidates may be based on the top K most frequently appearing n-grams. For example, the top-3 most frequently appearing n-grams may be selected as Aspects. The value of K may be configurable, including configurable by a user. 
     In a related embodiment, the selection of Aspects from n-grams of Aspect candidates may be based on such n-grams exceeding a threshold value. The threshold value may be configurable, including configurable by a user. For example, the method  300  may select any n-gram (the number of selected n-grams may be capped) whose frequency is above a threshold value. 
     Referring now to  FIG. 4 , a computing device  1000  may include respective sets of internal components  800  and external components  900 . The computing device  1000  may be or may include, for example, the Aspect Extraction System  100  ( FIG. 1 ). Each of the sets of internal components  800  includes one or more processors  820 ; one or more computer-readable RAMs  822 ; one or more computer-readable ROMs  824  on one or more buses  826 ; one or more operating systems  828 ; one or more software applications  828   a  (e.g., device driver modules) executing the program  102  ( FIG. 1 ); and one or more computer-readable tangible storage devices  830 . The one or more operating systems  828  and device driver modules are stored on one or more of the respective computer-readable tangible storage devices  830  for execution by one or more of the respective processors  820  via one or more of the respective RAMs  822  (which typically include cache memory). In the embodiment illustrated in  FIG. 4 , each of the computer-readable tangible storage devices  830  is a magnetic disk storage device of an internal hard drive. Alternatively, each of the computer-readable tangible storage devices  830  is a semiconductor storage device such as ROM  824 , EPROM, flash memory or any other computer-readable tangible storage device that can store a computer program and digital information. 
     Each set of internal components  800  also includes a R/W drive or interface  832  to read from and write to one or more computer-readable tangible storage devices  936  such as a thin provisioning storage device, CD-ROM, DVD, SSD, memory stick, magnetic tape, magnetic disk, optical disk or semiconductor storage device. The R/W drive or interface  832  may be used to load the device driver  840  firmware, software, or microcode to tangible storage device  936  to facilitate communication with components of computing device  1000 . 
     Each set of internal components  800  may also include network adapters (or switch port cards) or interfaces  836  such as a TCP/IP adapter cards, wireless WI-FI interface cards, or 3G or 4G wireless interface cards or other wired or wireless communication links. The operating system  828  that is associated with computing device  1000 , can be downloaded to computing device  1000  from an external computer (e.g., server) via a network (for example, the Internet, a local area network or wide area network) and respective network adapters or interfaces  836 . From the network adapters (or switch port adapters) or interfaces  836  and operating system  828  associated with computing device  1000  are loaded into the respective hard drive  830  and network adapter  836 . The network may comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. 
     Each of the sets of external components  900  can include a computer display monitor  920 , a keyboard  930 , and a computer mouse  934 . External components  900  can also include touch screens, virtual keyboards, touch pads, pointing devices, and other human interface devices. Each of the sets of internal components  800  also includes device drivers  840  to interface to computer display monitor  920 , keyboard  930  and computer mouse  934 . The device drivers  840 , R/W drive or interface  832  and network adapter or interface  836  comprise hardware and software (stored in storage device  830  and/or ROM  824 ). 
     Referring now to  FIG. 5 , an illustrative cloud computing environment  400  is depicted. As shown, the cloud computing environment  400  comprises one or more cloud computing nodes, each of which may be a computing system  1000  ( FIG. 4 ) with which local computing devices used by cloud consumers, such as, for example, a personal digital assistant (PDA) or a cellular telephone  400 A, a desktop computer  400 B, a laptop computer  400 C, and/or an automobile computer system  400 N, may communicate. The nodes  1000  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows the cloud computing environment  400  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  400 A-N shown in  FIG. 4  are intended to be illustrative only and that the computing nodes  1000  and the cloud computing environment  400  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG. 6 , a set of functional abstraction layers  500  provided by the cloud computing environment  400  ( FIG. 5 ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG. 6  are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided. 
     The hardware and software layer  510  includes hardware and software components. Examples of hardware components include mainframes, in one example IBM® zSeries® systems; RISC (Reduced Instruction Set Computer) architecture based servers, in one example IBM pSeries® systems; IBM xSeries® systems; IBM BladeCenter® systems; storage devices; networks and networking components. Examples of software components include network application server software, in one example IBM WebSphere® application server software; and database software, in one example IBM DB2® database software. (IBM, zSeries, pSeries, xSeries, BladeCenter, WebSphere, and DB2 are trademarks of International Business Machines Corporation registered in many jurisdictions worldwide). 
     The virtualization layer  514  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers; virtual storage; virtual networks, including virtual private networks; virtual applications and operating systems; and virtual clients. 
     In one example, the management layer  518  may provide the functions described below. Resource provisioning provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal provides access to the cloud computing environment for consumers and system administrators. Service level management provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     The workloads layer  522  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation; software development and lifecycle management; virtual classroom education delivery; data analytics processing; transaction processing; and Aspect extraction system ( FIGS. 1-3 ). 
     While the present invention is particularly shown and described with respect to preferred embodiments thereof, it will be understood by those skilled in the art that changes in forms and details may be made without departing from the spirit and scope of the present application. It is therefore intended that the present invention not be limited to the exact forms and details described and illustrated herein, but falls within the scope of the appended claims. 
     The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. 
     While steps of the disclosed method and components of the disclosed systems and environments have been sequentially or serially identified using numbers and letters, such numbering or lettering is not an indication that such steps must be performed in the order recited, and is merely provided to facilitate clear referencing of the method&#39;s steps. Furthermore, steps of the method may be performed in parallel to perform their described functionality.