Emotwitter – a fine-grained visualization system for identifying enduring sentiments in tweets

Abstract

Traditionally, work on sentiment analysis focuses on detecting the positive and negative attributes of sentiments. To broaden the scope, we introduce the concept of enduring sentiments based on psychological descriptions of sentiments as enduring emotional dispositions that have formed over time. To aid us identify the enduring sentiments, we present a fine-grained functional visualization system, EmoTwitter, that takes tweets written over a period of time as input for analysis. Adopting a lexicon-based approach, the system identifies the Plutchik’s eight emotion categories and shows them over the time period that the tweets were written. The enduring sentiment patterns of like and dislike are then calculated over the time period using the flow of the emotion categories. The potential impact and usefulness of our system are highlighted during a user-based evaluation. Moreover, the new concept and technique introduced in this paper for extracting enduring sentiments from text shows great potential, for instance, in business decision making.