Searching for the most negative opinions

Abstract

Studies in sentiment analysis and opinion mining have been focused on several aspects of opinions, such as their automatic extraction, identification of their polarity (positive, negative or neutral), the entities or facets involved, and so on. However, to the best of our knowledge, no sentiment analysis approach has considered the automatic identification and extraction of the most negative opinions, in spite of their significant impact in many fields such as industry, trade, political and socials issues. In this article, we will use diversified linguistic features and supervised machine learning algorithms so as to examine their effectiveness in the process of searching for the most negative opinions.