AUEB: Two Stage Sentiment Analysis of Social Network Messages

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 114–118, Dublin, Ireland, August 23-24, 2014. AUEB: Two Stage Sentiment Analysis of Social Network Messages Rafael Michael Karampatsis, John Pavlopoulos and Prodromos Malakasiotis mpatsis13@gmail.com, annis@aueb.gr, rulller@aueb.gr Department of Informatics Athens University of Economics and Business Patission 76, GR-104 34 Athens, Greece Abstract This paper describes the system submit- ted for the Sentiment Analysis in Twitter Task of SEMEVAL 2014 and specifically the Message Polarity Classification sub- task. We used a 2–stage pipeline approach employing a linear SVM classifier at each stage and several features including mor- phological features, POS tags based fea- tures and lexicon based features. 1 Introduction Recently, Twitter has gained significant popularity among the social network services. Lots of users often use Twitter to express feelings or opinions about a variety of subjects. Analysing this kind of content can lead to useful information for fields, such as personalized marketing or social profiling. However such a task is not trivial, because the lan- guage used in Twitter is often informal presenting new challenges to text analysis. In this paper we focus on sentiment analysis, the field of study that analyzes people’s sentiment and opinions from written language (Liu, 2012). Given some text (e.g., tweet), sentiment analysis systems return a sentiment label, which most often is positive, negative, or neutral. This classification can be performed directly or in two stages; in the first stage the system examines whether the text carries sentiment and in the second stage, the sys- tem decides for the sentiment’s polarity (i.e., posi- tive or negative).1 This decomposition is based on the assumption that subjectivity detection and sen- timent polarity detection are different problems. This work is licensed under a Creative Commons At- tribution 4.0 International Licence. Page numbers and pro- ceedings footer are added by the organisers. Licence details: http://creativecommons.org/licenses/by/4.0/ 1For instance a 2–stage approach is better suited to sys- tems that focus on subjectivity detection; e.g., aspect based sentiment analysis systems which extract aspect terms only from evaluative texts. We choose to follow the 2–stage approach, be- cause it allows us to focus on each of the two prob- lems separately (e.g., features, tuning, etc.). In the following we will describe the system with which we participated in the Message Polarity Classi- fication subtask of Sentiment Analysis in Twit- ter (Task 9) of SEMEVAL 2014 (Rosenthal et al., 2014). Specifically Section 2 describes the data provided by the organizers of the task. Sections 3 and 4 present our system and its performance re- spectively. Finally, Section 5 concludes and pro- vides hints for future work. 2 Data At first, we describe the data used for this year’s task. For system tuning the organizers released the training and development data of SEMEVAL 2013 Task 2 (Wilson et al., 2013). Both these sets are allowed to be used for training. The organizers also provided the test data of the same Task to be used for development only. As argued in (Malaka- siotis et …

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