PhD Defense: 'Corpus-based Construction of Sentiment Lexicon to Identify Extreme Opinions by Supervised and Unsupervised Machine learning Methods'

Studies in sentiment analysis and opinion mining focused on many aspects related to opinions, particularly polarity classification by making use of positive, negative or neutral values.

However, most studies overlooked the identification of extreme opinions (very negative and very positive opinions) in spite of their vast significance in many applications. This doctoral thesis describes a strategy to build sentiment lexicons from corpora, namely lexicons adapted to extreme values.

This strategy has been used to build some lexicons and to know its effectiveness in determining the polarity of opinions.