System and method for adapting sentiment analysis to user profiles to reduce bias

Abstract

Provided is a system and method for adapting analysis to user profiles to reduce bias in customer or user generated content, specifically a system and method that discounts or adjusts bias in sentiment data based on the channel from which the content was received and/or the demographic of the user. The system includes a means to detect bias for any product, service, or company across multiple channels of customer data; a means to construct models to quantize bias by specific demographics and channels; and a means to adjust model output to reduce inflation by biased groups.

What is claimed is: 
     
         1 . A method of improving objectivity of an outcome of regression analysis across a plurality of customer service channels, wherein each customer service channel is an electronic platform, the method including one or more processing devices performing operations comprising:
 building bias profiles for users in interactions across the plurality of customer service channels, wherein the customer service channel is chat, email, telephonic, or a social media platform; and the bias profiles comprise user demographic information;   grouping all of the interactions around a common product, topic or service to produce at least one grouping of interactions;   performing analysis on content on each customer service channel by each users to determine bias in a segment of the content to produce an original score;   determining an adjustment factor based on the bias and the bias profiles;   applying the adjustment factor to the original score to compensate for the bias; and   generating an adjusted score.   
     
     
         2 . The method of  claim 1 , the operations further comprising constructing a model of correlations between specific customer attributes, channels and bias. 
     
     
         3 . The method of  claim 1 , wherein applying the regression analysis is performed on content created on each customer service channel by each user. 
     
     
         4 . The method of  claim 3 , wherein the regression analysis is performed in parallel via a distributed computer cluster. 
     
     
         5 . The method of  claim 4 , wherein the parallel performing of the regression analysis is disturbed according to customer service channel. 
     
     
         6 . A system comprising:
 a processing device; and   a memory device in which instructions executable by the processing device are stored for causing the processor to:
 build bias profiles for users in interactions across the plurality of customer service channels, wherein the customer service channel is chat, email, telephonic, or a social media platform the bias profiles comprise user demographic information; 
 group all of the interactions around a common product, topic or service to produce at least one grouping of interactions; 
 perform analysis on content on each customer service channel by each users to determine bias in a segment of the content to produce an original score; 
 determine an adjustment factor based on the bias and the bias profiles; 
 apply the adjustment factor to the original score to compensate for the bias; and
 generate an adjusted score. 
 
   
     
     
         7 . The system of  claim 6 , the memory device further storing therein instructions executable for causing the processor to construct a model of correlations between specific customer attributes, channels and bias. 
     
     
         8 . The system of  claim 6 , wherein the regression analysis is performed on content created on each customer service channel by each user. 
     
     
         9 . The system of  claim 8 , wherein the regression analysis is performed in parallel via a distributed computer cluster. 
     
     
         10 . The system of  claim 9 , wherein the parallel performing of the regression analysis is disturbed according to channel. 
     
     
         11 . A non-transitory computer-readable storage medium having program code that is executable by a processor to cause a computing device to perform operations, the operations comprising:
 build bias profiles for users in interactions across the plurality of customer service channels, wherein the customer service channel is chat, email, telephonic, or a social media platform and the bias profiles comprise user demographic information;   group all of the interactions around a common product, topic or service to produce at least one grouping of interactions;   perform analysis on content on each customer service channel by each users to determine bias in a segment of the content to produce an original score;   determine an adjustment factor based on the bias and the bias profiles;   apply the adjustment factor to the original score to compensate for the bias; and   generate an adjusted score.   
     
     
         12 . The non-transitory computer-readable storage medium of  claim 11 , the operations further comprising constructing a model of correlations between specific customer attributes, channels and sentiment polarity. 
     
     
         13 . The non-transitory computer-readable storage medium of  claim 11 , wherein performing sentiment analysis comprises applying a regression analysis to content created on each channel by each user. 
     
     
         14 . The non-transitory computer-readable storage medium of  claim 13 , wherein the regression analysis is performed in parallel via a distributed compute cluster. 
     
     
         15 . The non-transitory computer-readable storage medium of  claim 14 , wherein the parallel performing of the regression analysis is disturbed according to channel.