Enhancement of SentiWordNet using contextual valence shifters Online publication date: Wed, 27-Nov-2019
by Poornima Mehta; Satish Chandra
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 11, No. 4, 2019
Abstract: Sentence structure has a considerable impact on the sentiment polarity of a sentence. In the presence of contextual valence shifters like conjunctions, conditionals and intensifiers some parts of the sentence are more relevant to determine the sentence polarity. In this work we have used valence shifters in sentences to enhance the sentiment lexicon SentiWordNet in a given document set. They have also been used to improve the sentiment analysis at document level. In the near past, micro blogging services like Twitter have become an important data source for sentiment analysis. Tweets, being restricted to 140 characters have slangs, are grammatically incorrect, have spelling mistakes and have informal expressions. The method is aimed at noisy and unstructured data like tweets on which computationally intensive tools like dependency parsers are not very successful. Our proposed system works better on both noisy (Stanford and airlines datasets of Twitter) and structured (movie review) datasets.
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