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Sentiment analysis has received much attention in Information Retrieval (IR) and other domains including data mining, machine learning algorithms and NLP. However, when it comes to big data, incorporating sentiment of words into IR models becomes even more important, and as yet no widely accepted standard exists for this task. The contribution of this paper is a framework for quantifying term frequency (TF) variants with sentiments. We propose models derived from the strength of lexical features to improve sentiment-based ranking.
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