TeamX: A Sentiment Analyzer with Enhanced Lexicon Mapping and Weighting Scheme for Unbalanced Data

摘要

This paper describes the system that has been used by TeamX in SemEval-2014 Task 9 Subtask B. The system is a sentiment analyzer based on a supervised text categorization approach designed with following two concepts. Firstly, since lexicon features were shown to be effective in SemEval-2013 Task 2, various lexicons and pre-processors for them are introduced to enhance lexical information. Secondly, since a distribution of sentiment on tweets is known to be unbalanced, an weighting scheme is introduced to bias an output of a machine learner. For the test run, the system was tuned towards Twitter texts and successfully achieved high scoring results on Twitter data, average F_1 70.96 on Twitter2014 and average F_1 56.50 on Twitter2014Sarcasm.