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We explore the relationship between negated text and neg- ative sentiment in the task of sentiment classiﬁcation. We propose a novel adjustment factor based on negation occur- rences as a proxy for negative sentiment that can be applied to lexicon-based classiﬁers equipped with a negation detec- tion pre-processing step. We performed an experiment on a multi-domain customer reviews dataset obtaining accuracy improvements over a baseline, and we further improved our results using out-of-domain data to calibrate the adjustment factor. We see future work possibilities in exploring nega- tion detection reﬁnements, and expanding the experiment to a broader spectrum of opinionated discourse, beyond that of customer reviews.
Ohana, B., Tierney, B. & Delany, S.J. (2016). Sentiment classification using negation as a proxy for negative sentiment.29th. International Florida Artificial Intelligence Research Society Conference