Title

Feature-Set for Sentiment analysis

Document Type

Conference Proceeding

Publication Date

4-1-2019

Department

Computer & Information Science

Abstract

For sentiment analysis, we address the problem of supervised-learning being domain-dependent. Additionally, we try to solve the limitations faced during unsupervised-learning where the bag-of-words (lexicon) are not reliable enough, as they might not cover the broad spectrum of words that represent sentiments. We try to overcome these limitations using a novel approach where we combine multiple lexicons and filter out the corpus using the lexicons, before forwarding it to the classifiers. As discussed in the results section, this approach led to the overall improvement in the accuracy of the classifiers. © 2019 IEEE.

DOI

10.1109/SoutheastCon42311.2019.9020656

Publication Title

Conference Proceedings - IEEE SOUTHEASTCON

ISBN

9781728101378

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