SWATAC: A Sentiment Analyzer using One-Vs-Rest Logistic Regression

摘要

This paper describes SWATAC, a system built for SemEval-2015's Task 10 Subtask B, namely the Message Polarity Classification Task. Given a tweet, the system classifies the sentiment as either positive, negative, or neutral. Several preprocessing tasks such as negation detection, spell checking, and tokeniza-tion are performed to enhance lexical information. The features are then augmented with external sentiment lexicons. Classification is done with Logistic Regression using a one-vs-rest configuration. For the test runs, the system was trained using only the provided training tweets. The classifier was successful, with an F1 score of 58.43 on the official 2015 test data, and an F1 score of 66.64 on the Twitter 2014 progress data.