| dc.description.abstract |
Selection and extraction of appropriate numerical features to do sentiment analysis on text data with greater accuracy remain an open problem. In supervised machine learning based sentiment analysis, Term Frequency- Inverse Document Frequency (TF-IDF) scores are used as a feature for classifying polarity of text data. TF-IDF features are a high dimensional representation of the importance of a word in the document. TF-IDF features are sparse and do not consider the correlation among the words which constructs the latent concepts in the document. Latent Semantic Analysis (LSA) removes sparseness of the TF-IDF features by representing it in a low dimensional matrix and extracts those hidden concepts. On the other hand, a natural property of text document is its information content. The quantitative estimation of Parts-of-Speech tags, negation words, sentiment lexicons etc. represent the quality of information shared in a text data. In this work, we propose an approach to generate a concept based domain specific feature set SentiCon by consolidating LSA with the quality of information of the corpus. We have applied Singular Value Decomposition (SVD) on TF-IDF features to find the LSA. We have tested SentiCon with two benchmark datasets IMDB movie review and Epinion Cars, Books datasets using four well-known classifiers - Decision Tree, Random Forrest, Support Vector Machine, and K-Nearest Neighbour classifiers. We have used standard performance measures precision, recall and F-measure to analyze the results. |
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