Highlighting keyphrases using senti-scoring and fuzzy entropy for unsupervised sentiment analysis. (1st May 2021) - Vashishtha, Srishti; Susan, Seba
Abstract: Sentiment Analysis is a process that aids in assessing the performance of products or services from user generated online posts. In present time, there are various websites that allow customers to post reviews about movies, products, events or services, etc. This has led to cumulative aggregation of a lot of reviews written in natural language. Prevailing factors such as availability of online reviews and raised end-user expectations have motivated the evolution of opinion mining systems that can automatically classify customers' reviews. It is observed that in Sentiment Analysis (SA), to highlight the significant keyphrases which contribute towards correct sentiment cognition is a tedious task. In this paper, we have proposed an unsupervised sentiment classification system that comprehensively formulates phrases, computes their senti-scores (sentiment scores) and polarity using the SentiWordNet lexicon and fuzzy linguistic hedges. Further it extracts the keyphrases significant for SA using fuzzy entropy filter and k-means clustering. We have deployed document level SA on online reviews using n-gram techniques, specifically combination of unigram, bigram and trigram. Experiments on two benchmark movie review datasets- polarity dataset by Pang and Lee and IMDB dataset, achieve high accuracy for our approach as compared to the other state-of-the-art-methods for phrase-level SA. Highlights: An unsupervised sentiment classification system using n-grams technique for online reviews Formulation and senti-scoring of phrase patterns Senti-scores of phrases are computed from SentiWordNet lexicon and fuzzy linguistic hedges. Applied Fuzzy Entropy filter and k-means clustering for extracting and highlighting keyphrases Results of comparison with other state-of-the-art indicate the higher scores of our system.