Abstract
This paper presents the system submitted by KUNLPLab for SemEval-2014 Task9 - Subtask B: Message Polarity on Twitter data. Lexicon features and bag-of-words features are mainly used to represent the datasets. We trained a logistic regression classifier and got an accuracy of 6% increase from the baseline feature representation. The effect of pre-processing on the classifier’s accuracy is also discussed in this work.
Cite
CITATION STYLE
Assefa, B. G. (2014). KUNLPLab:Sentiment Analysis on Twitter Data. In 8th International Workshop on Semantic Evaluation, SemEval 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014, Proceedings (pp. 391–394). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/s14-2067
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