Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings

摘要

Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a finegrained sentiment analysis. For example, master, seasoned and familiar point to different intensity levels, though they all convey the same meaning (semantics), i.e., expertise, having a good knowledge of In this paper, we propose a semi-supervised technique that uses sentiment bearing word embeddings to produce a continuous ranking among adjectives that share common semantics. Our system demonstrates a strong Spearman's rank correlation of 0 83 with the gold standard ranking. We show that sentiment bearing word embeddings facilitate a more accurate intensity ranking system than other standard word embeddings (word2vec and GloVe). Word2vec is the state-of-the-art for intensity ordering task.