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Sentiment-Bearing New Words Mining: Exploiting Emoticons and Latent Polarities

  • Fei WangEmail author
  • Yunfang Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9042)

Abstract

New words and new senses are produced quickly and are used widely in micro blogs, so to automatically extract new words and predict their semantic orientations is vital to sentiment analysis in micro blogs. This paper proposes Extractor and PolarityAssigner to tackle this task in an unsupervised manner. Extractor is a pattern-based method which extracts sentiment-bearing words from large-scale raw micro blog corpus, where the main task is to eliminate the huge ambiguities in the un-segmented raw texts. PolarityAssigner predicts the semantic orientations of words by exploiting emoticons and latent polarities, using a LDA model which treats each sentiment-bearing word as a document and each co-occurring emoticon as a word in that document. The experimental results are promising: many new sentiment-bearing words are extracted and are given proper semantic orientations with a relatively high precision, and the automatically extracted sentiment lexicon improves the performance of sentiment analysis on an open opinion mining task in micro blog corpus.

Keywords

new words new senses semantic orientation LDA model 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Key Laboratory of Computational Linguistics (Peking University)Ministry of EducationBeijingChina

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