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

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.