ELiRF: A SVM Approach for SA tasks in Twitter at SemEval-2015

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 574–581, Denver, Colorado, June 4-5, 2015. c⃝2015 Association for Computational Linguistics ELiRF: A Support Vector Machine Approach for Sentiment Analysis Tasks in Twitter at SemEval-2015 Mayte Gim´enez Ferran Pla Llu´ıs-F. Hurtado Universitat Polit`ecnica de Val`encia Cam´ı de Vera s/n, 46022 Val`encia {mgimenez,fpla,lhurtado}@dsic.upv.es Abstract This paper describes our participation at tasks 10 (sub-task B, Message Polarity Classifica- tion) and 11 task (Sentiment Analysis of Fig- urative Language in Twitter) of Semeval2015. We describe the Support Vector Machine sys- tem we used in this competition. We also present the relevant feature set that we take into account in our models. Finally, we show the results we obtained in this competition and some conclusions. 1 Introduction Nowadays social media, such as Twitter, produce a vast amount of information that lead us to new chal- lenges in Machine Learning (ML) and in Natural Language Processing (NLP) fields. Twitter 1 is a micro-blogging service, which accord- ing to latest statistics, has 284 million active users, 77 % outside the US that generate 500 million tweets a day in 35 different languages. That means 5,700 tweets per second and they had peaks of activity of 43,000 per second. This numbers justify the great interest in the automatic processing of this informa- tion. The study (Analytics, 2009) estimates that 50.9% of tweets have some useful information that are capable of mobilize opinions in Internet and also in the real world. Therefore, social media users opinions have great strategic value for different organizations. Our work is focused on automatically identify the prevailing sentiment in a tweet using ML and NLP 1About twitter,inc. https://about.twitter.com/company. Ac- cessed: 30-12-2014. techniques. We developed a system for determin- ing the tweets polarity for 10B and 11 tasks at the SemEval-2015 competition. The aim of task 10 (subtask B) (Rosenthal et al., 2015) is to classify tweets among positive, nega- tive, and neutral polarity. In task 11 (Ghosh et al., 2015) we had to deal with figurative language, and we should assign a polarity to each tweet with a score that vary in the range [-5..5], this score rep- resents the degree of the sentiment. Due to this last requirement, we formalized this task as a regression problem. Our approach shared some points for solving both tasks. Preprocessing and feature extraction pro- cesses from the corpora were similar. We considered some common problems when we are dealing with text from social media and in particular from Twit- ter: short texts, slang, peculiarities of the language (hashtags, retweets, user mentions, etc.). We rep- resented features extracted using a bag of n-grams. We used Support Vector Machine (SVM) formalism due to the fact to its ability to handle large feature space and to determine the relevant features. Task 10B has been considered as a classification problem and it has been modeled by means of SVM classifiers. For Task 11 we used regression SVM, due to the granularity of the scores. Both tasks were solved …

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