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

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 628–632, Dublin, Ireland, August 23-24, 2014. TeamX: A Sentiment Analyzer with Enhanced Lexicon Mapping and Weighting Scheme for Unbalanced Data Yasuhide Miura Fuji Xerox Co., Ltd. / Japan yasuhide.miura@fujixerox.co.jp Shigeyuki Sakaki Fuji Xerox Co., Ltd. / Japan sakaki.shigeyuki@fujixerox.co.jp Keigo Hattori Fuji Xerox Co., Ltd. / Japan keigo.hattori@fujixerox.co.jp Tomoko Ohkuma Fuji Xerox Co., Ltd. / Japan ohkuma.tomoko@fujixerox.co.jp Abstract This paper describes the system that has been used by TeamX in SemEval-2014 Task 9 Subtask B. The system is a senti- ment analyzer based on a supervised text categorization approach designed with fol- lowing two concepts. Firstly, since lex- icon 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 sys- tem was tuned towards Twitter texts and successfully achieved high scoring results on Twitter data, average F1 70.96 on Twit- ter2014 and average F1 56.50 on Twit- ter2014Sarcasm. 1 Introduction The growth of social media has brought a ris- ing interest to make natural language technologies that work with informal texts. Sentiment anal- ysis is one such technology, and several work- shops such as SemEval-2013 Task 2 (Nakov et al., 2013), CLEF 2013 RepLab 2013 (Amig´o et al., 2013), and TASS 2013 (Villena-Rom´an and Garc´ıa-Morera, 2013) have recently targeted tweets or cell phone messages as analysis text. This paper describes a system that has submit- ted a sentiment analysis result to Subtask B of SemEval-2014 Task9 (Rosenthal et al., 2014). SemEval-2014 Task9 is a rerun of SemEval-2013 Task 2 with different test data, and Subtask B is a task of message polarity classification. This work is licenced under a Creative Commons Attribution 4.0 International License. Page numbers and proceedings footer are added by the organizers. License details: http: //creativecommons.org/licenses/by/4.0/ The system we prepared is a sentiment ana- lyzer based on a supervised text categorization approach. Various features and their extraction methods are integrated in the system following the works presented in SemEval-2013 Task 2. Addi- tionally to these features, we assembled following notable functionalities to the system: 1. Processes to enhance word-to-lemma map- ping. (a) A spelling corrector to normalize out-of- vocabulary words. (b) Two Part-of-Speech (POS) taggers to realize word-to-lemma mapping in two perspectives. (c) A word sense disambiguator to obtain word senses and their confidence scores. 2. An weighting scheme to bias an output of a machine learner. Functionalities 1a to 1c are introduced to enhance information based on lexical knowledge, since features based on lexicons are shown to be ef- fective in SemEval-2013 Task 2 (Mohammad et al., 2013). Functionality 2 is introduced to make the system adjustable to polarity unbalancedness known to exists in Twitter data (Nakov et al., 2013). The accompanying sections of this papers are organized as follows. Section 2 …

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