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.
CITATION STYLE
Ameur, H., Jamoussi, S., & Ben Hamadou, A. (2018). A new emotional vector representation for sentiment analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9624 LNCS, pp. 258–269). Springer Verlag. https://doi.org/10.1007/978-3-319-75487-1_20
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