In this article, we propose a word-level classification model for automatically generating a Twitter-specific opinion lexicon from a corpus of unlabelled tweets. The tweets from the corpus are represented by two vectors: a bag-of-words vector and a semantic vector based on word-clusters. We propose a distributional representation for words by treating them as the centroids of the tweet vectors in which they appear. The lexicon generation is conducted by training a word-level classifier using these centroids to form the instance space and a seed lexicon to label the training instances. Experimental results show that the two types of tweet vectors complement each other in a statistically significant manner and that our generated lexicon produces significant improvements for tweet-level polarity classification.
Bravo-Marquez et al. REF propose word-level classification to show how to generate opinion lexicons from unlabelled tweets.
From Unlabelled Tweets to Twitter-specific Opinion Words
Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
Computer Science