Sentiment classification in English from sentence-levelannotations of emotions regarding models of affect

Abstract

This paper presents a text classifier for automatically taggingthe sentiment of input text according to the emotion that is beingconveyed. This system has a pipelined framework composedof Natural Language Processing modules for feature extractionand a hard binary classifier for decision making between posi-tive and negative categories. To do so, the Semeval 2007 datasetcomposed of sentences emotionally annotated is used for train-ing purposes after being mapped into a model of affect. Theresulting scheme stands a first step towards a complete emotionclassifier for a future automatic expressive text-to-speech syn-thesizer.