Linguistic Features to Identify Extreme Opinions: An Empirical Study

EasyChair Preprint 619

Abstract

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.

Keyphrases: Classification, Extreme Opinion, Opinion Mining, Sentiment Analysis, linguistic features, sentiment lexicon

@booklet{EasyChair:619,
  author    = {Sattam Almatarneh and Pablo Gamallo},
  title     = {Linguistic Features to Identify Extreme Opinions: An Empirical Study},
  doi       = {10.29007/7wzx},
  howpublished = {EasyChair Preprint 619},
  year      = {EasyChair, 2018}}