SAP-RI: Twitter Sentiment Analysis in Two Days

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 522–526, Dublin, Ireland, August 23-24, 2014. SAP-RI: Twitter Sentiment Analysis in Two Days Akriti Vij1,2∗, Nishtha Malhotra1,2∗, Naveen Nandan1 and Daniel Dahlmeier1 1SAP Research & Innovation, Singapore 2Nanyang Technological University, Singapore {akriti.vij,nishtha.malhotra,naveen.nandan,d.dahlmeier}@sap.com Abstract We describe the submission of the SAP Research & Innovation team to the Se- mEval 2014 Task 9: Sentiment Analy- sis in Twitter. We challenged ourselves to develop a competitive sentiment anal- ysis system within a very limited time frame. Our submission was developed in less than two days and achieved an F1 score of 77.26% for contextual polar- ity disambiguation and 55.47% for mes- sage polarity classification, which shows that rapid prototyping of sentiment anal- ysis systems with reasonable accuracy is possible. 1 Introduction Microblogging platforms and social networks have become increasingly popular for expressing opinions on a wide range of topics, hence mak- ing them valuable and ever-growing logs of pub- lic sentiment. This has motivated the development of automatic natural language processing (NLP) methods to analyse the sentiment expressed in these short, informal messages (Liu, 2012; Pang and Lee, 2008). In particular, the study of sentiment and opin- ions in messages from the Twitter microblogging platform has attracted a lot of interest (Jansen et al., 2009; Pak and Paroubek, 2010; Barbosa and Feng, 2010; O’Connor et al., 2010; Bifet et al., 2011). However, comparative evaluations of senti- ment analysis of Twitter messages have previously been hindered by the lack of a large benchmark data set. The goal of the SemEval 2013 task 2: Sentiment Analysis in Twitter (Nakov et al., 2013) ∗ The work was done during an internship at SAP. This work is licenced under a Creative Commons Attribution 4.0 International License. Page numbers and proceedings footer are added by the organizers. License details: http: //creativecommons.org/licenses/by/4.0/ and this year’s continuation in the SemEval 2014 task 9: Sentiment Analysis in Twitter (Rosenthal et al., 2014) is to close this gap by hosting a shared task competition which provided a large corpus of Twitter messages which are annotated with sen- timent polarity labels. The task consists of two subtasks: in subtask A contextual polarity disam- biguation, participants need to predict the polarity of a given word or phrase in the context of a tweet message, in subtask B message polarity classifica- tion, participants need to predict the dominating sentiment of the complete message. Both tasks consider sentiment analysis to be a three-way clas- sification problem between positive, negative, and neutral sentiment. In this paper, we describe the submission of the SAP-RI team to the SemEval 2014 task 9. We challenged ourselves to develop a competitive sen- timent analysis system within a very limited time frame. The complete system was implemented within only two days. Our system is based on supervised classification with support vector ma- chines with lexical and dictionary-based features. Our system achieved an F1 score of 77.26% for contextual polarity disambiguation and 55.47% for message polarity classification. Although our scores are …

Content truncated to 500 words