US 11,675,823 B2
Sentiment analysis for aspect terms extracted from documents having unstructured text data
Ramakanth Kanagovi, Bengaluru (IN); Sumant Sahoo, Bengaluru (IN); Arun Swamy, Bengaluru (IN); Ravi Shukla, Bengaluru (IN); Prakash Sridharan, Bengaluru (IN); Shrikrishna K. Joisa, Bengaluru (IN); Sandeep Ratnakar, Bengaluru (IN); and Mayank Sharma, Kanpur (IN)
Assigned to Dell Products L.P., Round Rock, TX (US)
Filed by Dell Products L.P., Round Rock, TX (US)
Filed on Oct. 13, 2021, as Appl. No. 17/500,042.
Prior Publication US 2023/0112589 A1, Apr. 13, 2023
Int. Cl. G06F 16/00 (2019.01); G06F 16/35 (2019.01); G06N 20/20 (2019.01); G06F 16/33 (2019.01)
CPC G06F 16/353 (2019.01) [G06F 16/3346 (2019.01); G06N 20/20 (2019.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus comprising:
at least one processing device comprising a processor coupled to a memory;
the at least one processing device being configured to perform steps of:
receiving a query to perform sentiment analysis for a document, the document comprising unstructured text data;
generating, utilizing a first machine learning model, a first set of encodings of the unstructured text data of the document, the first set of encodings classifying each word of the unstructured text data of the document as being an aspect term or a non-aspect term;
generating, utilizing a second machine learning model, a second set of encodings of the unstructured text data of the document, the second set of encodings classifying sentiment of each word of the unstructured text data of the document;
determining, for a given aspect term corresponding to a given sequence of one or more of the words of the unstructured text data of the document classified as an aspect term in the first set of encodings, attention weights for a given subset of words in the unstructured text data surrounding the given sequence of the one or more words;
generating, utilizing a third machine learning model, a given sentiment classification of the given aspect term, the third machine learning model generating the given sentiment classification based at least in part on (i) the attention weights for the given subset of the words in the unstructured text data surrounding the given sequence of the one or more words and (ii) a given portion of the second set of encodings classifying the sentiment of the given subset of the words in the unstructured text data surrounding the given sequence of the one or more words; and
providing a response to the query, the response to the query comprising the given sentiment classification of the given aspect term.