Sentiment analysis tuning

Abstract

In one example, the present disclosure describes a device, computer-readable medium, and method for sentiment analysis tuning. In one example, the method, includes acquiring a first sentiment analysis-generated score for a first string of text, wherein the first string of text includes a first plurality of words, and wherein the first sentiment analysis-generated score is calculated using a set of first values associated with the first plurality of words, calculating a second value for at least one word of the first plurality of words, based on a non-sentiment-analysis generated score associated with the first string of text, acquiring a second string of text, wherein the second string of text includes a second plurality of words, and wherein the second plurality of words includes the at least one word, and calculating a sentiment analysis-generated score for the second string of text, using the second value for the at least one word.

What is claimed is: 
     
         1 . A method comprising:
 acquiring, by a processing system including at least one processor, a first sentiment analysis-generated score for a first string of text, wherein the first string of text includes a first plurality of words, and wherein the first sentiment analysis-generated score is calculated using a set of first values associated with the first plurality of words;   calculating, by the processing system, a second value for at least one word of the first plurality of words, based on a first non-sentiment-analysis generated score associated with the first string of text;   acquiring, by the processing system, a second string of text, wherein the second string of text includes a second plurality of words, and wherein the second plurality of words includes the at least one word; and   calculating, by the processing system, a second sentiment analysis-generated score for the second string of text, using the second value for the at least one word.   
     
     
         2 . The method of  claim 1 , wherein the first non-sentiment analysis generated score comprises a numerical score automatically derived from non-text features of a multi-modal input associated with the string of text. 
     
     
         3 . The method of  claim 2 , wherein the multi-modal input comprises an audible input. 
     
     
         4 . The method of  claim 2 , wherein the multi-modal input comprises a visual input. 
     
     
         5 . The method of  claim 2 , wherein the numerical score is generated by:
 representing the non-text features as a vector; and   assigning the numerical score to the vector using a deep learning technique.   
     
     
         6 . The method of  claim 1 , wherein the first non-sentiment-analysis generated score comprises a numerical rating provided by a same source as the string of text. 
     
     
         7 . The method of  claim 1 , wherein each value of the set of first values comprises a weight that implies a magnitude of a sentiment expressed by a corresponding word of the first plurality of words. 
     
     
         8 . The method of  claim 7 , wherein the second value comprises an adjusted weight that implies a magnitude of a sentiment expressed by the at least one word. 
     
     
         9 . The method of  claim 7 , wherein the first sentiment analysis-generated score is calculated by combining values of the set of first values that correspond to the first plurality of words. 
     
     
         10 . The method of  claim 1 , wherein the calculating the second value for the at least one word is performed using a multidimensional optimization problem that finds a set of second values including the second value, and wherein the set of second values, when applied to the first plurality of words, maximizes a correlation between the first sentiment analysis-generated score and the first non-sentiment analysis-generated score. 
     
     
         11 . The method of  claim 10 , wherein the multidimensional optimization problem is tuned for a specific domain. 
     
     
         12 . The method of  claim 1 , wherein the second string of text comprises a survey response provided by a customer of a service provider. 
     
     
         13 . The method of  claim 1 , further comprising:
 predicting a second non-sentiment analysis-generated score associated with the second string of text, using the second value.   
     
     
         14 . The method of  claim 13 , wherein the second non-sentiment analysis-generated score is a net promoter score. 
     
     
         15 . The method of  claim 14 , wherein the second value is used to establish a correlation between a plurality of sentiment analysis-generated scores and a plurality of net promoter scores. 
     
     
         16 . A device comprising:
 a processor; and   a non-transitory computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations comprising:
 acquiring a first sentiment analysis-generated score for a first string of text, wherein the first string of text includes a first plurality of words, and wherein the first sentiment analysis-generated score is calculated using a set of first values associated with the first plurality of words; 
 calculating a second value for at least one word of the first plurality of words, based on a non-sentiment-analysis generated score associated with the first string of text; 
 acquiring a second string of text, wherein the second string of text includes a second plurality of words, and wherein the second plurality of words includes the at least one word; and 
 calculating a sentiment analysis-generated score for the second string of text, using the second value for the at least one word. 
   
     
     
         17 . A non-transitory computer-readable medium storing instructions which, when executed by a processor, cause the processor to perform operations, the operations comprising:
 acquiring a first sentiment analysis-generated score for a first string of text, wherein the first string of text includes a first plurality of words, and wherein the first sentiment analysis-generated score is calculated using a set of first values associated with the first plurality of words;   calculating a second value for at least one word of the first plurality of words, based on a non-sentiment-analysis generated score associated with the first string of text;   acquiring a second string of text, wherein the second string of text includes a second plurality of words, and wherein the second plurality of words includes the at least one word; and   calculating a sentiment analysis-generated score for the second string of text, using the second value for the at least one word.   
     
     
         18 . The non-transitory computer-readable medium of  claim 17 , wherein the first non-sentiment analysis generated score comprises a numerical score automatically derived from non-text features of a multi-modal input associated with the string of text. 
     
     
         19 . The non-transitory computer-readable medium of  claim 18 , wherein the multi-modal input comprises an audible input or a visual input. 
     
     
         20 . The non-transitory computer-readable medium of  claim 18 , wherein the numerical score is generated by:
 representing the non-text features as a vector; and   assigning the numerical score to the vector using a deep learning technique.