An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification

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Semih Sevim 1,* Sevinc ilhan Omurca 1 Ekin Ekinci 1

1. Kocaeli University Computer Engineering Departmen, Kocaeli, TURKEY

* Corresponding author.


Received: 22 Sep. 2017 / Revised: 22 Oct. 2017 / Accepted: 7 Nov. 2017 / Published: 8 Jan. 2018

Index Terms

Twitter sentiment classification, ensemble learning, Semantic enrichment, BabelNet


With the widespread usage of social media in our daily lives, user reviews emerged as an impactful factor for numerous fields including understanding consumer attitudes, determining political tendency, revealing strengths or weaknesses of many different organizations. Today, people are chatting with their friends, carrying out social relations, shopping and following many current events through the social media. However social media limits the size of user messages. The users generally express their opinions by using emoticons, abbreviations, slangs, and symbols instead of words. This situation makes the sentiment classification of social media texts more complex. In this paper a sentiment classification model for Twitter messages is proposed to overcome this difficulty. In the proposed model first the short messages are expanded with BabelNet which is a concept network. Then the expanded and the original form of the messages are included in an ensemble learning model. Consequently we compared our ensemble model with traditional classification algorithms and observed that the F-measure value is increased.

Cite This Paper

Semih Sevim, Sevinç İlhan Omurca, Ekin Ekinci, "An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification", International Journal of Information Technology and Computer Science(IJITCS), Vol.10, No.1, pp.24-31, 2018. DOI:10.5815/ijitcs.2018.01.03


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