Abstract

Message-level and word-level polarity classification are two popular tasks in Twitter sentiment analysis. They have been commonly addressed by training supervised models from labelled data. The main limitation of these models is the high cost of data annotation. Transferring existing labels from a related problem domain is one possible solution for this problem. In this paper, we propose a simple model for transferring sentiment labels from words to tweets and vice versa by representing both tweets and words using feature vectors residing in the same feature space. Tweets are represented by standard NLP features such as unigrams and part-of-speech tags. Words are represented by averaging the vectors of the tweets in which they occur. We evaluate our approach in two transfer learning problems: 1) training a tweet-level polarity classifier from a polarity lexicon, and 2) inducing a polarity lexicon from a collection of polarity-annotated tweets. Our results show that the proposed approach can successfully classify words and tweets after transfer.