Sentiment analysis tuning

Patent metadata

Field	Value
Publication number	US-2021081617-A1
Application number	US-202017107898-A
Country	US
Kind code	A1
Filing date	Nov 30, 2020
Priority date	Oct 2, 2018
Publication date	Mar 18, 2021
Grant date	—

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.

First claim

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.

Assignees

• At & T Ip I Lp

Inventors

• Jacobson Guy
• Hsu Wen-Ling
• Xu Tan

Classifications

• G06V30/274

  Syntactic or semantic context, e.g. balancing · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06F18/23

  Clustering techniques · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/09

  Supervised learning · CPC title