id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_65xfr2qt4zboda2elkpprp4zra	Anil Bandhakavi	Emotion-aware polarity lexicons for Twitter sentiment analysis	2018	20	.pdf	application/pdf	7158	712	57	the usefulness of an emotion-rich corpus of documents (e.g. tweets) to learn polarity lexicons for sentiment analysis. We propose two different methods that leverage a corpus of emotion-labelled tweets to learn word-polarity lexicons. Sentiment analysis concerns the computational study of natural language text (e.g. words, sentences and documents) in order to identify and effectively quantify its 1. We propose two different methods to generate sentiment lexicons from a corpus of emotion-labelled tweets by combining our prior work on domain-specific an emotion-labelled corpus of tweets to learn sentiment lexicons. emotion-labelled twitter corpus [37] in this study for generating sentiment lexicons. from a corpus of emotion labelled tweets using methods such as latent dirichlet allocation (LDA) can be used to learn emotion-aware sentiment lexicons. Emotion-aware polarity lexicons for Twitter sentiment analysis. Emotion-aware polarity lexicons for Twitter sentiment analysis. Emotion-aware polarity lexicons for Twitter sentiment analysis. Emotion-aware polarity lexicons for Twitter sentiment analysis.	./cache/work_65xfr2qt4zboda2elkpprp4zra.pdf	./txt/work_65xfr2qt4zboda2elkpprp4zra.txt