Studies in sentiment analysis and opinion mining have examined how different features are effective in polarity classification by making use of positive, negative or neutral values. However, the identification of extreme opinions (most negative and most positive opinions) have overlooked in spite of their wide significance in many applications. In our study, we will combine empirical features (e.g. bag of words, word embeddings, polarity lexicons, and set of textual features) so as to identify extreme opinions and provide a comprehensive analysis of the relative importance of each set of features using hotel reviews.
CITATION STYLE
Almatarneh, S., & Gamallo, P. (2018). Linguistic Features to Identify Extreme Opinions: An Empirical Study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11314 LNCS, pp. 215–223). Springer Verlag. https://doi.org/10.1007/978-3-030-03493-1_23
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