Sentiment or Opinion Mining aims to determine the polarity of people’s opinions, feeling towards any product, service, event or any individual. One of the most popular technique applied in sentiment analysis of textual content is natural language processing. Sentiment can be evaluated using numerous methodologies like machine learning algorithms and statistical tools but the use of the fuzzy concept is not common. In this paper, we analyze the effect of fuzzification of word polarity sentiment scores. These word scores are obtained by deploying two lexicons: SentiWordNet and AFINN. Experiments are conducted on three benchmark datasets: polarity movie dataset by Pang-Lee, IMDB and hotel reviews dataset. The key highlights are: i) proposed an unsupervised fuzzy logic-based approach for sentiment analysis of textual reviews, ii) the proposed model formulated fuzzy cardinality as the measure for the evaluation of word polarity scores, iii) our model has two versions based on the sentiment lexicon deployed in the model, iv) comparison of our fuzzy cardinality approach with other non- fuzzy state-of-the-art methods reveals the superiority of our fuzzy approach.
Dataset
Movie Review Dataset : IMDB. Each movie review has several sentences. The IMDB dataset has a training set of 25,000 labeled instances and a testing set of 25,000 labeled instances; the dataset has positive and negative labels balanced in training and testing set. Another movie review dataset : polarity dataset v2.0 by Pang and Lee. It contains 1000 positive and 1000 negative reviews.
Lexicon We have used SentiWordNet and AFINN lexicon.
Citation
If using this code, please cite our work using :
S. Vashishtha and S. Susan, "Fuzzy Interpretation of Word Polarity Scores for Unsupervised Sentiment Analysis," 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2020, pp. 1-6, doi: 10.1109/ICCCNT49239.2020.9225646.