Patent Publication Number: US-2022237216-A1

Title: Impression evaluation apparatus, impression evaluation method and program

Description:
TECHNICAL FIELD 
     The present invention relates to an impression evaluation device, an impression evaluation method, and a program. 
     BACKGROUND ART 
     Technology for evaluating visual impression of text by experimentation and analyzing trends in evaluation by impression is being studied. There also is proposed technology for automatically estimating impression of new expressions regarding words that are not text. 
     For example, in NPL 1, in order to evaluate the visual impression of an overall text, handwritten text is presented to experiment participants, evaluation is performed on a seven-point scale by pairs of adjectives, and principal component analysis is performed to quantify the impression. 
     Also, in NPL 2, a system is proposed in which an evaluation experiment of impressions of onomatopoeia (imitative words, mimetic words) is performed, and the impression of a new onomatopoeia that is generated can be estimated on the basis of results thereof. 
     CITATION LIST 
     Non Patent Literature 
     
         
         [NPL 1] Yukie Tsuzuki, Noriko Shingaki, “Cognitive dimensions when perceiving others&#39; handwriting”, 26th conference of Japanese Cognitive Science Society, p 2-19, 2009. 
         [NPL 2] Yuichiro Shimizu, Ryuichi Doizaki, Maki Sakamoto, “A System to Estimate an Impression Conveyed by Onomatopoeia”, Journal of The Japanese Society for Artificial Intelligence, Vol. 29, No. 1, SPI-E, pp. 41-52, 2014. 
       
    
     SUMMARY OF THE INVENTION 
     Technical Problem 
     In NPL 1, impression evaluation regarding one type of text prepared for experimentation is performed. Accordingly, tendencies of impressions regarding each pattern of text can be extracted by repeatedly performing similar experiments, but impressions regarding optional new text cannot be estimated. Accordingly, the method according to NPL 1 has a problem in that experimentation needs to be performed each time, and the method accordingly cannot be generalized. 
     In NPL 2, impressions of optional new onomatopoeic expressions are predicted on the basis of tendencies of impressions of onomatopoeia. However, features of onomatopoeia are subliminal notions evoked by phonology and words such as fluffy=soft, while visual impressions of assemblies of letters need elements regarding appearance such as the shape, weight, and so forth, of the letters, as feature values thereof, and accordingly this method cannot be applied as it is to prediction of visual impressions of text. 
     The present invention has been made in view of the foregoing points, and it is an object hereof to enable quantitative evaluation of impressions of text strings. 
     Means for Solving the Problem 
     Accordingly, in order to solve the above problems, an impression evaluation device includes a first calculating unit that calculates values of a plurality of items regarding feature values of an input text string, and a second calculation unit that calculates, on the basis of a value calculated by the first calculating unit regarding one item out of the plurality of items selected on the basis of correlation between each of the plurality of items and an index evaluating an impression of a letter, and a storage unit storing a value of the index for each letter and values of the plurality of items, a value for the index with regard to the text string. 
     Effects of the Invention 
     Impressions of text strings can be quantitatively evaluated. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram illustrating a hardware configuration example of an impression evaluation device  10  according to an embodiment of the present invention. 
         FIG. 2  is a diagram illustrating a functional configuration example of the impression evaluation device  10  according to the embodiment of the present invention. 
         FIG. 3  is a flowchart for describing an example of processing procedures executed by the impression evaluation device  10 . 
         FIG. 4  is a diagram illustrating a configuration example of a letter evaluation value DB  16 . 
         FIG. 5  is a diagram illustrating a configuration example of a correlation value DB  15 . 
         FIG. 6  is a diagram for describing an example of formulating an evaluation axis and distribution of a corresponding physical feature item. 
         FIG. 7  is a diagram illustrating a configuration example of a change value DB  17 . 
         FIG. 8  is a diagram illustrating output examples of impression evaluation values. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     An embodiment of the present invention will be described below with reference to the Figures. In the present embodiment, a technology is disclosed in which impressions of optional text strings such as words, text, and so forth (hereinafter referred to as “letter assembly”) are estimated from impressions evaluation values of letters that are already evaluated, taking into consideration the degree of effects of ornamentation of the letters such as typeface, color, and so forth. Note that an impressions evaluation value is a values of an index relating to estimation of evaluation of impression of letters. 
       FIG. 1  is a diagram illustrating a hardware configuration example of an impression evaluation device  10  according to the embodiment of the present invention. The impression evaluation device  10  in  FIG. 1  has a drive device  100 , an auxiliary storage device  102 , a memory device  103 , a CPU  104 , an interface device  105 , a display device  106 , and an input device  107  and so forth, which are connected to each other by a bus B. 
     A program that realizes the processing at the impression evaluation device  10  is provided by a recording medium  101  such as a CD-ROM. When the recording medium  101  storing the program is set in the drive device  100 , the program is installed in the auxiliary storage device  102  from the recording medium  101  via the drive device  100 . Note however, that installation of the program does not necessarily need to be performed by the recording medium  101 , and may be downloaded from another computer via a network. The auxiliary storage device  102  stores the installed program, and also stores necessary files, data, and so forth. 
     In a case where there is a start instruction for the program, the memory device  103  reads out the program from the auxiliary storage device  102  and stores the program. The CPU  104  realizes the functions relating to the impression evaluation device  10  following the program stored in the memory device  103 . The interface device  105  is used as an interface for connecting to a network. The display device  106  displays a GUI (Graphical User Interface) or the like, in accordance with the program. The input device  107  is configured of a keyboard and a mouse or the like, and is used for inputting various operation instructions. 
       FIG. 2  is a diagram illustrating a functional configuration example of the impression evaluation device  10  according to the embodiment of the invention. In  FIG. 2 , the impression evaluation device  10  has a physical feature value calculating unit  11 , an evaluation axis deciding unit  12 , a reference evaluation value calculating unit  13 , and an impression evaluation value calculating unit  14  and so forth. These units are realized by processing of one or more programs installed in the impression evaluation device  10  being executed by the CPU  104 . The impression evaluation device  10  also uses databases (storage units) such as a correlation value DB  15 , a letter evaluation value DB  16 , and a change value DB  17  and so forth. These databases can be realized by using, for example, the auxiliary storage device  102 , or a storage device that can be connected to the impression evaluation device  10  via a network, or the like. 
     The processing procedures that the impression evaluation device  10  executes will be described below.  FIG. 3  is a flowchart for describing an example of processing procedures that the impression evaluation device  10  executes. 
     In step S 101 , the physical feature value calculating unit  11  accepts input of a letter assembly (a text string of an optional number of letters that is one or more) that is the object of calculation of an impression evaluation value, ornamentation to be added to the letter assembly, and an a subjective evaluation value to be used as an evaluation axis for the impression evaluation value (hereinafter, referred to simply as “evaluation axis”). The letter assembly may be input as text via the input device  107  or the like, or content input by audio may be input by being converted into text, for example. It is sufficient for the input letter assembly to be an assembly of letters, and may be kanji, katakana, alphanumericals, Arabian, or the like. 
     Input of ornamentation is optional, and in a case where specifying the font, color, or the like of the letter assembly that is to be the object of evaluation is desired, the user may select from a font list, text color list, or the like, from a pulldown menu. Alternatively, the ornamentation may be selected at the same time as when inputting the text serving as the letter assembly. 
     The evaluation axis is information regarding what sort of perspective (i.e., on the basis of which subjective evaluation value as a reference) knowing the impression evaluation value of the letter assembly is desired. Input of the evaluation axis is optional, and the user may be presented with a list of subjective evaluation values contained in the letter evaluation value DB  16 , such as softness, powerfulness, and so forth, to select from a pulldown menu, for example. 
       FIG. 4  is a diagram illustrating a configuration example of the letter evaluation value DB  16 . As illustrated in  FIG. 4 , the letter evaluation value DB  16  stores, for each object of evaluation, such as the hiragana syllabary, a subjective evaluation value table T 1  including items of a plurality of types of subjective evaluation values with regard to the objects of evaluation, a table T 2  including values of items of a plurality of types of physical feature values with regard to the objects of evaluation, which are created and stored in advance. It is sufficient for the object of evaluation to be letters, and may include katakana, alphanumericals, and so forth. 
     Subjective evaluation values are average values or the like, obtained by indeterminate evaluators subjectively evaluating each of an optional number of items that is one or more regarding impressions of the visual appearance of letters, on a five-point scale, or the like, and averaging values relating to evaluation of impressions of the letters. For example, items making up subjective evaluation values include one or more of softness, powerfulness, and sharpness. Note however, that these items are not fundus, as long as items by which the degree of impression of the letters can be evaluated. 
     Physical feature values are objective observation values regarding an optional number of items that is one or more regarding the visual appearance of the object of evaluation. Items making up the physical feature values include, for example, one or more of curvature, which represents the degree of curving of letters, the complexity of the shapes, and so forth. Note however, that this is not limited to particular items, as long as items that can be observed and quantified in one way or another. For example, with regard to curvature, the degree of curving of each letter can be calculated by a method described in “Takanori Komatsu, Satoshi Nakamura, Masaaki Suzuki, ‘Don&#39;t you think Hiragana is rounder than Katakana?’: Availabilities of Mathematical Representations of Characters and its Curvature, Information Processing Society of Japan Technical Report, Vol. 2014-HCI-159, No. 7, 2014”, or the like. Also, complexity of letters can be calculated using software that can calculate the percentage of black within an image (e.g., “GIMP” https://www.gimp.org/) or the like. 
     Note that the values of each of the items of the subjective evaluation values and physical feature values are normalized to values within a range of 0 to 1, and stored in the letter evaluation value DB  16 . 
     Next, the physical feature value calculating unit  11  calculates the physical feature values of the letter assembly that has been input (hereinafter referred to as “input letter assembly”) (S 102 ). A method that is the same as known technology that may be used for calculating curvature, complexity, and so forth, at the time of creating the letter evaluation value DB  16 , may be used for calculating these physical features. Note however, that in step S 102 , each of the physical feature values such as curvature, complexity, and so forth, is calculated for the input letter assembly, and not one letter at a time. 
     Next, the evaluation axis deciding unit  12  references the correlation value DB  15  and decides the evaluation axis for calculating the impression evaluation value for the input letter assembly (S 103 ). This evaluation axis is selected from the items of the plurality of subjective evaluation values making up the columns in the table T 1  of the letter evaluation value DB  16 . 
       FIG. 5  is a diagram illustrating the configuration example of the correlation value DB  15 . As illustrated in  FIG. 5 , for each combination of each item in the subjective evaluation values and each item in the physical feature values, a value representing the height of correlation between the items regarding the combination (hereinafter referred to as “correlation value”) is stored in the correlation value DB  15  in advance. Note that in  FIG. 5 , the items of subjective evaluation values are laid out in the column direction, and the items of physical feature values are laid out in the row direction. The correlation value for each combination may be calculated using a value group (number sequence) of the letters regarding the item for the subjective evaluation value regarding this combination, and a value group (number sequence) of the letters regarding the item for the physical feature values regarding this combination, in the letter evaluation value DB  16 . For example, the Excel CORREL function or the like may be used to calculate a correlation function as the correlation value. Note however, that the calculation method is optional, and another method may be used as long as a method for calculating the height of correlation among number sequences. 
     In step S 103 , in a case where an item for a subjective evaluation value to serve as an evaluation axis is specified by the user as input, the evaluation axis deciding unit  12  first selects the item of the physical feature values of which the correlation with this evaluation axis is the highest (hereinafter referred to as “corresponding physical features item”). For example, in a case where “softness” is specified as the evaluation axis, the “curvature” of which the correlation with “softness” is highest in the example in  FIG. 5  is selected as the corresponding physical features item. Conversely, in a case where no evaluation axis is specified, the evaluation axis deciding unit  12  selects, with regard to the combination in which the correlation value is highest between the item in the subjective evaluation values and the item in the physical feature values, the item in the subjective evaluation values as the evaluation axis, and selects this item from the physical feature values as the corresponding physical features item. In the example in  FIG. 5 , selection is made of evaluation axis=softness and corresponding physical features item=curvature, of which the correlation value is the highest at 0.8. The evaluation axis deciding unit  12  outputs the decided evaluation axis and the corresponding physical features item to the evaluation axis deciding unit  12 . 
     Next, the reference evaluation value calculating unit  13  takes the evaluation axis and the corresponding physical features item output from the evaluation axis deciding unit  12  as input, and references the letter evaluation value DB  16  to calculate an impression evaluation value for the input letter assembly for a case of not taking ornamentation into consideration (hereinafter referred to as “reference evaluation value”) (S 104 ). Specifically, the reference evaluation value calculating unit  13  references values stored in the letter evaluation value DB  16  with regard to the evaluation axis and the corresponding physical features item that are input, and formulates the distribution of the evaluation axis and the corresponding physical features item. 
       FIG. 6  is a diagram for describing an example of formulating the evaluation axis and distribution of the corresponding physical features item. In the graph in  FIG. 6 , the vertical axis is an item of a subjective evaluation value serving as the evaluation axis (e.g., “softness”), and the horizontal axis is the corresponding physical features item (e.g., “curvature”). Each plot in this graph is based on each value of the evaluation axis and the corresponding feature value feature item of each evaluation reference (each letter) in the letter evaluation value DB  16 . That is to say, one plot corresponds to one evaluation reference (letter). 
     As illustrated in  FIG. 6 , the reference evaluation value calculating unit  13  may estimate a straight line from the coordinates distribution of plotted points by a method such as the method of least squares, for example, obtain a value for the evaluation axis for the input letter assembly that has been input, on the basis of the value of the corresponding physical features item calculated in step S 102  and this straight line, and take this value of the evaluation axis as the reference evaluation value of the input letter assembly. Note that estimation of formulation (straight line) may be performed in advance. That is to say, formulation may be performed for each set of subjective evaluation value item and physical feature value item, and a parameter indicating the straight line obtained by formulation may be stored in the auxiliary storage device  102  in advance. In this case, it is sufficient for the reference evaluation value calculating unit  13  to read out the parameter corresponding to the evaluation axis and the corresponding physical features item, and calculate the reference evaluation value for the input letter assembly on the basis of the straight line defined by this parameter. 
     Next, the impression evaluation value calculating unit  14  determines whether or not the ornamentation is specified by the user (S 105 ). In a case where ornamentation is not specified (Yes in S 105 ), the impression evaluation value calculating unit  14  substitutes the reference evaluation value calculated in step S 104  into the subjective evaluation value of the input letter assembly (S 106 ). 
     Conversely, in a case where ornamentation is specified by the user (No in S 105 ), the impression evaluation value calculating unit  14  references the change value DB  17 , and executes processing for reflecting the change amount of impression (hereinafter referred to as “variation value”) due to this ornamentation (hereinafter referred to as “specified ornamentation”) in the reference evaluation value of the input letter assembly (i.e., changing the reference evaluation value on the basis of the specified ornamentation). 
       FIG. 7  is a diagram illustrating a configuration example of the change value DB  17 . As illustrated in  FIG. 7 , the change value DB  17  stores the difference in impression evaluation values between a case of adding ornamentation to each evaluation reference (each letter) and a case of not doing so (hereinafter referred to as “change value”), in each table by type of ornamentation. Ornamentation represents the difference as to the evaluation object used when creating the letter evaluation value DB  16 , such as change in the font of the letter, change in letter color, and so forth. In  FIG. 7 , a table T 3  is a table in which is registered the change value for each evaluation reference (each letter) regarding ornamentation of “font=Meiryo”. A table T 4  is a table in which is registered the change value for each evaluation reference (each letter) regarding ornamentation of “letter color=green”. 
     First, the impression evaluation value calculating unit  14  references the column of the evaluation axis in the table corresponding to the specified ornamentation in the change value DB  17 , and calculates the change value (S 107 ). For example, in a case where the specified ornamentation is “font=Meiryo”, and the evaluation axis is “softness”, the impression evaluation value calculating unit  14  calculates the correlation between the number sequence of the column “softness” in the table T 3  in  FIG. 7  and the number sequence of the column of the corresponding physical features item in table T 2  in the letter evaluation value DB  16 , using the Excel CORREL function or the like. In a case where there is a correlation between the specified ornamentation and the corresponding physical features item (a condition is set such as absolute value of correlation coefficient≥0.5, for example), the impression evaluation value calculating unit  14  formulates the distribution regarding the specified ornamentation and the corresponding physical features item in the same way as in step S 104 , identifies the change value corresponding to the physical feature values of the input letter assembly, and takes this change value as the variation value. Note that formulation may be performed in advance, in the same way as in step S 104 . Alternatively, the impression evaluation value calculating unit  14  may calculate the average value of a change value group of a column corresponding to the specified ornamentation as the change value. 
     In a case where there is a plurality of specified ornamentations, (e.g., a case where the font is Meiryo and the letter color is green, or the like), the impression evaluation value calculating unit  14  calculates the variation value by a method of calculating the variation value for each specified ornamentation using one of the methods above, and using the variation value with the larger absolute value, taking the average value or the added value, or the like. 
     Following this, the impression evaluation value calculating unit  14  substitutes the results of adding the variation value to the reference evaluation value into the impression evaluation value (S 108 ). Following step S 106  or S 108 , the impression evaluation value calculating unit  14  outputs the impression evaluation value (S 109 ). For example, a numerical value as the impression evaluation value, and the evaluation axis at the time of the impression evaluation, may be output. 
       FIG. 8  is a diagram illustrating output examples of impression evaluation values.  FIG. 8  shows an output example of the impression evaluation value for each letter assembly that is input.  FIG. 8  shows an example in which the impression evaluation values are output in an “evaluation axis: impression evaluation value” format. Note however, that the output format of the impression evaluation value is not limited to a predetermined format. 
     As described above, according to the present embodiment, impressions of optional letter assemblies (text strings) can be quantitatively evaluated. As a result, when presenting information in which the impression of the visual appearance is important, such as signages and so forth, the burden of conceiving designs of letters in text and so forth by one&#39;s self or the trouble of commissioning this to a designer can be reduced, and also the trouble of performing impression evaluation experimentation at the time of coming up with product names and so forth can be reduced. 
     Note that in the present embodiment, the physical feature value calculating unit  11  is an example of a first calculating unit. The reference evaluation value calculating unit  13  is an example of a second calculating unit. The evaluation axis deciding unit  12  is an example of a selecting unit. The impression evaluation value calculating unit  14  is an example of a changing unit. The feature values are an example of the physical feature values. 
     Although an embodiment of the present invention has been described above in detail, the present invention is not limited to this particular embodiment, and various types of modifications and alterations may be made within the scope of the essence of the present invention set forth in Claims. 
     REFERENCE SIGNS LIST 
     
         
           10  Impression evaluation device 
           11  Physical feature value calculating unit 
           12  Evaluation axis deciding unit 
           13  Reference evaluation value calculating unit 
           14  Impression evaluation value calculating unit 
           15  Correlation value DB 
           16  Letter evaluation value DB 
           17  Change value DB 
           100  Drive device 
           101  Recording medium 
           102  Auxiliary storage device 
           103  Memory device 
           104  CPU 
           105  Interface device 
           106  Display device 
           107  Input device 
         B Bus