Simpler is better? Lexicon-based ensemble sentiment classification beats supervised methods
Łukasz Augustyniak, Tomasz Jan Kajdanowicz, Piotr Szymański, Włodzimierz Tuligłowicz, Przemysław Kazienko, Reda Alhajj, Boleslaw Karol Szymanski · 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) · 2014
It has been shown in this paper that simplistic Bag of Words (BoW) lexicon methods for sentiment polarity assignment with ensemble classifiers are much faster than a supervised approach to sentiment classification while yielding similar accuracy. BoW methods also proved to be efficient and fast across all examined datasets. Moreover, a new approach to lexicon extraction that can be successfully used for sentiment polarity assignment is presented in the paper. It has been shown that accuracy obtained from such lexicons outperforms other lexicon based approaches.