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Exploring Feature Definition and Selection for Sentiment Classifiers
Conference proceeding   Open access

Exploring Feature Definition and Selection for Sentiment Classifiers

Yelena Mejova and Padmini Srinivasan
Proceedings of the ... International AAAI Conference on Weblogs and Social Media, Vol.5(1), pp.546-549
08/03/2021
DOI: 10.1609/icwsm.v5i1.14163
url
https://doi.org/10.1609/icwsm.v5i1.14163View
Published (Version of record) Open Access

Abstract

In this paper, we systematically explore feature definition and selection strategies for sentiment polarity classification. We begin by exploring basic questions, such as whether to use stemming, term frequency versus binary weighting, negation-enriched features, n-grams or phrases. We then move onto more complex aspects including feature selection using frequency-based vocabulary trimming, part-of-speech and lexicon selection (three types of lexicons), as well as using expected Mutual Information (MI). Using three product and movie review datasets of various sizes, we show, for example, that some techniques are more beneficial for larger datasets than the smaller. A classifier trained on only few features ranked high by MI outperformed one trained on all features in large datasets, yet in small dataset this did not prove to be true. Finally, we perform a space and computation cost analysis to further understand the merits of various feature types.

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