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2018 | OriginalPaper | Buchkapitel

A New Emotional Vector Representation for Sentiment Analysis

verfasst von : Hanen Ameur, Salma Jamoussi, Abdelmajid Ben Hamadou

Erschienen in: Computational Linguistics and Intelligent Text Processing

Verlag: Springer International Publishing

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Abstract

With the advent of Web 2.0, social networks (like, Twitter and Facebook) offer to users a different writing style that’s close to the SMS language. This language is characterized by the presence of emotion symbols (emoticons, acronyms and exclamation words). They often manifest the sentiments expressed in the comments and bring an important contextual value to determine the general sentiment of the text. Moreover, these emotion symbols are considered as multilingual and universal symbols. This fact has inspired us to research in the area of automatic sentiment classification. In this paper, we present a new vector representation of text which can faithfully translate the sentimental orientation of text, based on the emotion symbols. We use Support Vector Machines to show that our emotional vector representation significantly improves accuracy for sentiment analysis problem compared with the well known bag-of-words vector representations, using dataset derived from Facebook.

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Vorheriges Kapitel Aspect Based Sentiment Analysis: Category Detection and Sentiment Classification for Hindi

Nächstes Kapitel Cascading Classifiers for Twitter Sentiment Analysis with Emotion Lexicons

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Titel: A New Emotional Vector Representation for Sentiment Analysis
verfasst von: Hanen Ameur
Salma Jamoussi
Abdelmajid Ben Hamadou
Verlag: Springer International Publishing
Buch: Computational Linguistics and Intelligent Text Processing
Print ISBN: 978-3-319-75486-4

Electronic ISBN: 978-3-319-75487-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-75487-1_20

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