SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of Features

Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pages 694–699, Vancouver, Canada, August 3 - 4, 2017. c⃝2017 Association for Computational Linguistics SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of Features Mohammed Jabreel Antonio Moreno Intelligent Technologies for Advanced Knowledge Acquisition (ITAKA), Departament d’Enginyeria Inform`atica i Matem`atiques, Universitat Rovira i Virgili, Av. Pa¨ısos Catalans, 26, 43007 Tarragona, Spain <first name>.<last name>@urv.cat Abstract This paper describes SiTAKA, our system that has been used in task 4A, English and Arabic languages, Sentiment Analysis in Twitter of SemEval2017. The system pro- poses the representation of tweets using a novel set of features, which include a bag of negated words and the information pro- vided by some lexicons. The polarity of tweets is determined by a classifier based on a Support Vector Machine. Our system ranks 2nd among 8 systems in the Arabic language tweets and ranks 8th among 38 systems in the English-language tweets. 1 Introduction Sentiment analysis in Twitter is the problem of identifying people’s opinions expressed in tweets. It normally involves the classification of tweets into categories such as positive, negative and in some cases, neutral. The main challenges in de- signing a sentiment analysis system for Twitter are the following: • Twitter limits the length of the message to 140 characters, which leads users to use novel abbreviations and often disregard standard sentence structures. • The informal language and the numerous spelling errors. Most of the existing systems are inspired by the work presented in (Pang et al., 2002). Ma- chine Learning techniques have been used to build a classifier from a set of tweets with a manually annotated sentiment polarity. The success of the Machine Learning models is based on two main facts: a large amount of labeled data and the in- telligent design of a set of features that can distin- guish between the positive, negative and neutral samples. With this approach, most studies have focused on designing a set of efficient features to obtain a good classification performance (Feldman, 2013; Liu, 2012; Pang and Lee, 2008). For instance, the authors in (Mohammad et al., 2013) used diverse sentiment lexicons and a variety of hand-crafted features. This paper proposes the representation of tweets using a novel set of features, which include the in- formation provided by seven lexicons and a bag of negated words (BonW). The concatenation of these features with a set of basic features improves the classification performance. The polarity of tweets is determined by a classifier based on a Support Vector Machine. The system has been evaluated on the Arabic and English language test sets of the Twitter Sen- timent Analysis Track in SemEval 2017, subtask A (Message Polarity Classification). Our system (SiTAKA) has been ranked 8th over 38 teams in the English language test set and 2nd out of 8 teams in the Arabic language test set. The rest of the paper is structured as follows. Section 2 presents the tools and the resources that …

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