UDLAP: Sentiment Analysis Using a Graph-Based Representation

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 556–560, Denver, Colorado, June 4-5, 2015. c⃝2015 Association for Computational Linguistics UDLAP: Sentiment Analysis Using a Graph Based Representation Esteban Castillo1, Ofelia Cervantes1, Darnes Vilari˜no2, David B´aez1 and Alfredo S´anchez1 1Universidad de las Am´ericas Puebla Department of Computer Science, Electronics and Mechatronics, Mexico {esteban.castillojz, ofelia.cervantes}@udlap.mx {david.baez, alfredo.sanchez}@udlap.mx 2Benem´erita Universidad Aut´onoma de Puebla Faculty of Computer Science, Mexico darnes@cs.buap.mx Abstract We present an approach for tackling the Sen- timent Analysis problem in SemEval 2015. The approach is based on the use of a co- occurrence graph to represent existing rela- tionships among terms in a document with the aim of using centrality measures to extract the most representative words that express the sentiment. These words are then used in a su- pervised learning algorithm as features to ob- tain the polarity of unknown documents. The best results obtained for the different datasets are: 77.76% for positive, 100% for negative and 68.04% for neutral, showing that the pro- posed graph-based representation could be a way of extracting terms that are relevant to de- tect a sentiment. 1 Introduction In the past decade, new forms of communication, such as microblogging and text messaging have emerged and become ubiquitous. While there is no limit to the range of information conveyed by tweets and texts, often these short messages are used to share opinions and sentiments that people have about what is going on in the world around them. Working with these informal text genres presents challenges for natural language processing (NLP) beyond those encountered when working with more traditional text genres. Typically this kind of texts are short and the language used is very informal. We can find creative spelling and punctuation, slang, new words, URLs, and genre-specific terminology and abbreviations that make their manipulation more challenging. Representing that kind of text for automatically mining and understanding the opinions and senti- ments that people communicate inside them has very recently become an attractive research topic (Pang, 2008). In this sense, the experiments reported in this paper were carried out in the framework of the SemEval 20151 (Semantic Evaluation) which has created a series of tasks for Sentiment Analysis on Twitter (Rosenthal, 2015). Among the proposed tasks we find Task 10, subtask B which was named Message Polarity Classification and was defined as follows: ”Given a message, classify whether the message is of positive, negative, or neutral senti- ment. For messages conveying both a positive and a negative sentiment, whichever is the stronger sen- timent should be chosen”. In order to solve this task we create an approach that uses a graph based rep- resentation to extract relevant words that are used in a supervised learning method to classify a set of unknown documents in different topics and genres provided by the SemEval team. The methodology for our approach is discussed in detail in the next sections. The rest of the paper is structured as follows. In Section 2 we present some related …

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