Sentiment analysis system adaptation for multilingual processing: The case of tweets
Information Processing & ManagementPublished 3 December 2014
Alexandra Balahur, José M. Perea‐Ortega
Citations53
SJR quartileQ1
SJR score2.06
SNIP2.91
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TL;DR
It is shown that the use of hybrid features and multilingual, machine-translated data (even from other languages) can help to better distinguish relevant features for sentiment classification and thus increase the precision of sentiment analysis systems.
Abstract
Nowadays opinion mining systems play a strategic role in different areas such as Marketing, Decision Support Systems or Policy Support. Since the arrival of the Web 2.0, more and more textual documents containing information that express opinions or comments in different languages are available. Given the proven importance of such documents, the use of effective multilingual opinion mining systems has become of high importance to different fields. This paper presents the experiments carried out with the objective to develop a multilingual sentiment analysis system. We present initial evaluations of methods and resources performed in two international evaluation campaigns for English and for Spanish.\nAfter our participation in both competitions, additional experiments were carried out with the aim of improving the performance of both Spanish and English systems by using multilingual machine-translated data. Based on our evaluations, we show that the use of hybrid features and multilingual, machine-translated data (even from other languages) can help to better distinguish relevant features for sentiment classification and thus increase the precision of sentiment analysis systems.
Keywords
Computer Science
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