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Successes and challenges in developing a hybrid approach to sentiment analysis
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A Hybrid Approach to Sentiment Analysis with Benchmarking Results
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Abstract:
The objective of this article is two-fold. Firstly, a hybrid approach to Sentiment Analysis encompassing the use of Semantic Rules, Fuzzy Sets and an enriched Sentiment Lexicon, improved with the support of SentiWordNet is described. Secondly, the proposed hybrid method is compared against two well established Supervised Learning techniques, Naïve Bayes and Maximum Entropy. Using the well known and publicly available Movie Review Dataset, the proposed hybrid system achieved higher accuracy and precision than Naïve Bayes (NB) and Maximum Entropy (ME).
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Keyword:
Computational Linguistic; Fuzzy Sets; Natural Language Processing; Semantic Rules; Sentiment Analysis; SentiWordNet
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URL: http://hdl.handle.net/2086/11859
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A Hybrid Approach to the Sentiment Analysis Problem at the Sentence Level
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