Interpreting SentiWordNet for Opinion Classification

We describe a set of tools, resources, and experiments for opinion classification in business-related datasources in two languages. In particular we concentrate on SentiWordNet text interpretation to produce word, sentence, and text-based sentiment features for opinion classification. We achieve good results in experiments using supervised learning machine over syntactic and sentiment-based features. We also show preliminary experiments where the use of summaries before opinion classification provides competitive advantage over the use of full documents.

• Type: Conference
• Year: 2010
• Where: LREC
• Authors: Horacio Saggion, Adam Funk
• Keywords: Business-related Datasources, Education, LREC 2010, Opinion Classification, Text-based Sentiment Features