Exploring emoticons in polarity classification of text

With people increasingly using emoticons in written text on the Web 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 we subsequently propose and evaluate a novel method for exploiting this with a manually created emoticon sentiment lexicon in a lexicon-based polarity classification method. We evaluate our approach on 2,080 Dutch tweets and forum messages, which all contain emoticons. We validate our findings on 10,069 English reviews of apps, some of which contain emoticons. We find that accounting for the sentiment conveyed by emoticons on a paragraph level -- and, to a lesser extent, on a sentence level -- significantly improves polarity classification performance. Whenever emoticons are used, their associated sentiment tends to dominate the sentiment conveyed by textual cues and forms a good proxy for the polarity of text

Lexicon-based sentient analysis by mapping conveyed sentiment to intended sentiment

As consumers nowadays generate increasingly more content describing their experiences with, e.g., products and brands in various languages, information systems monitoring a universal, language-independent measure of peoples intended sentiment are crucial for todays businesses. In order to facilitate sentiment analysis of user-generated content, we propose to map sentiment conveyed by unstructured natural language text to universal star ratings, capturing intended sentiment. For these mappings, we consider a monotonically increasing step function, a naïve Bayes method, and a support vector machine. We demonstrate that the way in which natural language reveals intended sentiment differs across our datasets of Dutch and English texts. Additionally, the results of our experiments on modelling the relation between conveyed sentiment and intended sentiment suggest that language-specific sentiment scores can separate universal classes of intended sentiment from one another to a limited extent. Copyrigh