UMCC_DLSI-(SA): Using a ranking algorithm and informal features to solve Sentiment Analysis in Twitter

Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Seventh International Workshop on Semantic Evaluation (SemEval 2013), pages 443–449, Atlanta, Georgia, June 14-15, 2013. c⃝2013 Association for Computational Linguistics UMCC_DLSI-(SA): Using a ranking algorithm and informal features to solve Sentiment Analysis in Twitter Yoan Gutiérrez, Andy González, Roger Pérez, José I. Abreu University of Matanzas, Cuba {yoan.gutierrez, roger.perez ,jose.abreu}@umcc.cu, andy.gonzalez@infonet.umcc.cu Antonio Fernández Orquín, Alejandro Mosquera, Andrés Montoyo, Rafael Muñoz University of Alicante, Spain antonybr@yahoo.com, {amosquera, montoyo, rafael}@dlsi.ua.es Franc Camara Independent Consultant USA info@franccamara .com Abstract In this paper, we describe the development and performance of the supervised system UMCC_DLSI-(SA). This system uses corpora where phrases are annotated as Positive, Negative, Objective, and Neutral, to achieve new sentiment resources involving word dictionaries with their associated polarity. As a result, new sentiment inventories are obtained and applied in conjunction with detected informal patterns, to tackle the challenges posted in Task 2b of the Semeval- 2013 competition. Assessing the effectiveness of our application in sentiment classification, we obtained a 69% F-Measure for neutral and an average of 43% F-Measure for positive and negative using Tweets and SMS messages. 1 Introduction Textual information has become one of the most important sources of data to extract useful and heterogeneous knowledge from. Texts can provide factual information, such as: descriptions, lists of characteristics, or even instructions to opinion- based information, which would include reviews, emotions, or feelings. These facts have motivated dealing with the identification and extraction of opinions and sentiments in texts that require special attention. Many researchers, such as (Balahur et al., 2010; Hatzivassiloglou et al., 2000; Kim and Hovy, 2006; Wiebe et al., 2005) and many others have been working on this and related areas. Related to assessment Sentiment Analysis (SA) systems, some international competitions have taken place. Some of those include: Semeval-2010 (Task 18: Disambiguating Sentiment Ambiguous Adjectives 1 ) NTCIR (Multilingual Opinion Analysis Task (MOAT 2)) TASS 3 (Workshop on Sentiment Analysis at SEPLN workshop) and Semeval-2013 (Task 2 4 Sentiment Analysis in Twitter) (Kozareva et al., 2013). In this paper, we introduce a system for Task 2 b) of the Semeval-2013 competition. 1.1 Task 2 Description In participating in “Task 2: Sentiment Analysis in Twitter” of Semeval-2013, the goal was to take a given message and its topic and classify whether it had a positive, negative, or neutral sentiment towards the topic. For messages conveying, both a positive and negative sentiment toward the topic, the stronger sentiment of the two would end up as the classification. Task 2 included two sub-tasks. Our team focused on Task 2 b), which provides two training corpora as described in Table 3, and two test corpora: 1) sms-test-input-B.tsv (with 2094 SMS) and 2) twitter-test-input-B.tsv (with 3813 Twit messages). The following section shows some background approaches. Subsequently, in section 3, we describe the UMCC_DLSI-(SA) system that was used in Task 2 b). Section 4 describes the assessment of the obtained resource from the Sentiment Classification task. Finally, the conclusion and future works are …

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