TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier

Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pages 659–663, Vancouver, Canada, August 3 - 4, 2017. c⃝2017 Association for Computational Linguistics TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier Abstract This paper describes the TWINA system, with which we participated in SemEval-2017 Task 4B (Topic Based Message Polarity Classification – Two point scale) and 4D (Two-point scale Tweet Quantification). We implemented ensemble based Gradient Boost Trees Classification method for both the tasks. Our system could perform well for the task 4D and ranked 13th among 15 teams, for the task 4B our model ranked 23rd position. 1 Introduction Twitter, as a social networking service and microblogging service has gained great success in the recent years. It attracted millions of users to disseminate most up-to-date information, which resulted in generating massive amounts of information every day. Users share their opinions and experience on Twitter with the limit of 140 characters length text called as Tweet. Many applications in the field of Natural Language processing (NLP) and Information Retrieval (IR) are suffering severely from noisy in such a short 140 character length text. This paper describes the system, with which we participated in Task 4 (Sentiment Analysis in Twitter) of SemEval – 2017 (Rosenthal et al., 2017). Organizers have given five different subtasks in task 4, they are:  Task-4A: Message Polarity Classification  Task-4B: Two-point scale Topic Based Message Polarity Classification  Task-4C: Five-point scale Topic-Based Message Polarity Classification  Task-4D: Two-point scale-Tweet quantification  Task-4E: Five-point scale - Tweet quantification We participated in only two subtasks B and D. With our submissions, we could stand in 13th position among 15 participants of task 4D and ranked 23rd position in task 4B. For both the tasks B and D, we implemented basic model of ensemble based Gradient Boost Tree Classifier and applied parameter optimization technique to improve the results. The rest of the paper is organized as follows: In section 2 we describe the datasets, section 3 pre- processing of data for analysis, section 4 describes the model implementation using ensemble based Gradient Boost Trees Classification technique, section 5 gives results and section 6 gives conclusion and future work. 2 Datasets In implementing the solution for SemEval Task 4, for every subtask the organizers provide training, development testing and testing datasets for training and testing. In addition to, the organizers made 2015 datasets available for training and tuning. We have used 4896 tweets for training the model and 20632 to test the model during the development. Final test of the model has been done on 12284 tweets. Naveen Kumar Laskari Suresh Kumar Sanampudi Assistant Professor of IT Head of the Department, IT BVRIT Hyderabad JNTUH College of Engineering Jagitial naveen.laskari@gmail.com sureshsanampudi@gmail.com 659 3 Pre-processing Twitter has a constraint that, Tweet should not exceed 140 characters to convey the information or message. This makes the users to use unpredictable ways of expressing themselves. To find out sentiment from these kinds of tweets is …

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