Contextual semantics for sentiment analysis of Twitter

In this paper we propose a semantic approach to automatically identify and remove stopwords from Twitter data. Unlike most existing approaches, which rely on outdated and context-insensitive stopword lists, our proposed approach considers the contextual semantics and sentiment of words in order to measure their discrimination power. Evaluation results on 6 Twitter datasets show that, removing our semantically identified stopwords from tweets, increases the binary sentiment classification performance over the classic pre-complied stopword list by 0.42% and 0.94% in accuracy and F-measure respectively. Also, our approach reduces the sentiment classifier’s feature space by 48.34% and the dataset sparsity

• Type: Journal
• Year: 2016
• Where: IPM
• Authors: Hassan Saif, Yulan He, Miriam Fernández, Harith Alani
• Keywords: IPM 2016