As people increasingly use emoticons in text in order to express, stress, or disambiguate their sentiment, it is crucial for automated sentiment analysis tools to correctly account for such graphical cues for sentiment. We analyze how emoticons typically convey sentiment and demonstrate how we can exploit this by using a novel, manually created emoticon sentiment lexicon in order to improve a state-of-the-art lexicon-based sentiment classification method. We evaluate our approach on 2,080 Dutch tweets and forum messages, which all contain emoticons and have been manually annotated for sentiment. On this corpus, paragraph-level accounting for sentiment implied by emoticons significantly improves sentiment classification accuracy. This indicates that whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by textual cues and forms a good proxy for intended sentiment.
Sentiment analysis has applications in many areas and the exploration of its potential has only just begun. We propose Pathos, a framework which performs document sentiment analysis (partly) based on a document's discourse structure. We hypothesize that by splitting a text into important and less important text spans, and by subsequently making use of this information by weighting the sentiment conveyed by distinct text spans in accordance with their importance, we can improve the performance of a sentiment classifier. A document's discourse structure is obtained by applying Rhetorical Structure Theory on sentence level. When controlling for each considered method's structural bias towards positive classifications, weights optimized by a genetic algorithm yield an improvement in sentiment classification accuracy and macro-level F1 score on documents of 4.5% and 4.7%, respectively, in comparison to a baseline not taking into account discourse structure.
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