Title

SWASH: A Naive Bayes Classifier For Tweet Sentiment Identification

Document Type

Conference Proceeding

Publication Date

2015

Published In

Proceedings Of The 9th International Workshop On Semantic Evaluation

Abstract

This paper describes a sentiment classification system designed for SemEval-2015, Task 10, Subtask B. The system employs a constrained, supervised text categorization approach. Firstly, since thorough preprocessing of tweet data was shown to be effective in previous SemEval sentiment classification tasks, various preprocessessing steps were introduced to enhance the quality of lexical information. Secondly, a Naive Bayes classifier is used to detect tweet sentiment. The classifier is trained only on the training data provided by the task organizers. The system makes use of external human-generated lists of positive and negative words at several steps throughout classification. The system produced an overall F-score of 59.26 on the official test set.

Conference

9th International Workshop On Semantic Evaluation

Conference Dates

June 4-5, 2015

Conference Location

Denver, CO