A Robust User Sentiment Biterm Topic Mixture Model Based on User Aggregation Strategy to Avoid Data Sparsity for Short Text.

Sentiment analysis is a process of computationally finding the opinions that are expressed in a short text or a feedback by a writer towards a particular topic, product, service. The short piece of review from the user can help a business determine or understand the attitude of the user thereby predict the customer's behaviour and itsubstantiallyimproves the quality of service parameters. The proposed Robust User Sentiment Biterm Topic Mixture (RUSBTM)model discovers the user preference and their sentiment orientation views for effective Topic Modelling using Biterms or word-pair from the short text of a particular venue. Since short review or text suffers from data sparse, the user aggregation strategy is adapted to form a pseudo document and the word pairset is created for the whole corpus. The RUSBTM learns topics by generating the word co-occurrence patterns thereby inferring topics with rich corpus-level information. By analysing the sentiments of the paired words and their corresponding topics in the review corpus of the particular venue, prediction can be done that exactly portrays the user interest, preference and expectation from a particular venue. The RUSBTM model proved to be more robust and also, the extracted topics are more coherent and informative. Also the method uses accurate sentiment polarity techniques to exactly capture the sentiment orientation and the model proves to be outperforming better when compared to other state of art methods.