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In this project we propose a new approach for emotion recognition usingweb-based similarity (e.g. confidence, PMI and PMING). We aim to extract basicemotions from short sentences with emotional content (e.g. news titles, tweets,captions), performing a web-based quantitative evaluation of semantic proximitybetween each word of the analyzed sentence and each emotion of a psychologicalmodel (e.g. Plutchik, Ekman, Lovheim). The phases of the extraction include:text preprocessing (tokenization, stop words, filtering), search engineautomated query, HTML parsing of results (i.e. scraping), estimation ofsemantic proximity, ranking of emotions according to proximity measures. Themain idea is that, since it is possible to generalize semantic similarity underthe assumption that similar concepts co-occur in documents indexed in searchengines, therefore also emotions can be generalized in the same way, throughtags or terms that express them in a particular language, ranking emotions.Training results are compared to human evaluation, then additional comparativetests on results are performed, both for the global ranking correlation (e.g.Kendall, Spearman, Pearson) both for the evaluation of the emotion linked toeach single word. Different from sentiment analysis, our approach works at adeeper level of abstraction, aiming at recognizing specific emotions and notonly the positive/negative sentiment, in order to predict emotions as semanticdata.

Web-based Semantic Similarity for Emotion Recognition in Web Objects