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Extending the EmotiNet Knowledge Base to Improve the Automatic Detection of Implicitly Expressed Emotions from Text
BibTeX
@MISC{Balahur_extendingthe,
author = {Alexandra Balahur and Jesús M. Hermida},
title = {Extending the EmotiNet Knowledge Base to Improve the Automatic Detection of Implicitly Expressed Emotions from Text},
year = {}
}
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Abstract
Sentiment analysis is one of the recent, highly dynamic fields in Natural Language Processing. Although much research has been performed in this area, most existing approaches are based on word-level analysis of texts and are mostly able to detect only explicit expressions of sentiment. However, in many cases, emotions are not expressed by using words with an affective meaning (e.g. happy), but by describing real-life situations, which readers (based on their commonsense knowledge) detect as being related to a specific emotion. Given the challenges of detecting emotions from contexts in which no lexical clue is present, in this article we present a comparative analysis between the performance of well-established methods for emotion detection (supervised and lexical knowledge-based) and a method we extend, which is based on commonsense knowledge stored in the EmotiNet knowledge base. Our extensive comparative evaluations show that, in the context of this task, the approach based on EmotiNet is the most appropriate.
Keyphrases
emotinet knowledge base automatic detection commonsense knowledge explicit expression lexical clue existing approach well-established method affective meaning emotion detection many case sentiment analysis extensive comparative evaluation real-life situation dynamic field natural language processing comparative analysis much research word-level analysis specific emotion
