2024-11-0920149781-9416-4324-2N/AN/Ahttps://hdl.handle.net/20.500.14288/6697This 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. © 8th International Workshop on Semantic Evaluation, SemEval 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014, Proceedings.EngineeringComputer ScienceArtificial intelligenceConference proceedinghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85122028045&partnerID=40&md5=7f41b3e5401a5fdbce071fdfde76e6f4451