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Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis

Department of Computational Intelligence, Wrocław University of Technology, Wybrzeże Stanisława Wyspiańskiego 27, Wrocław 50-370, Poland
Illimites Foundation, Gajowicka 64 lok. 1, Wrocław 53-422, Poland
Authors to whom correspondence should be addressed.
This paper is an extended version of our paper published in the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Beijing, China, 17–20 August 2014.
Academic Editors: J. A. Tenreiro Machado and Kevin H. Knuth
Entropy 2016, 18(1), 4;
Received: 10 August 2015 / Revised: 24 November 2015 / Accepted: 15 December 2015 / Published: 25 December 2015
(This article belongs to the Section Complexity)
PDF [1445 KB, uploaded 25 December 2015]


We propose a novel method for counting sentiment orientation that outperforms supervised learning approaches in time and memory complexity and is not statistically significantly different from them in accuracy. Our method consists of a novel approach to generating unigram, bigram and trigram lexicons. The proposed method, called frequentiment, is based on calculating the frequency of features (words) in the document and averaging their impact on the sentiment score as opposed to documents that do not contain these features. Afterwards, we use ensemble classification to improve the overall accuracy of the method. What is important is that the frequentiment-based lexicons with sentiment threshold selection outperform other popular lexicons and some supervised learners, while being 3–5 times faster than the supervised approach. We compare 37 methods (lexicons, ensembles with lexicon’s predictions as input and supervised learners) applied to 10 Amazon review data sets and provide the first statistical comparison of the sentiment annotation methods that include ensemble approaches. It is one of the most comprehensive comparisons of domain sentiment analysis in the literature. View Full-Text
Keywords: sentiment analysis; opinion mining; machine learning; ensemble classification; sentiment lexicon generation sentiment analysis; opinion mining; machine learning; ensemble classification; sentiment lexicon generation

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Augustyniak, Ł.; Szymański, P.; Kajdanowicz, T.; Tuligłowicz, W. Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis. Entropy 2016, 18, 4.

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