Paper: Experiments with DBpedia, WordNet and SentiWordNet as resources for sentiment analysis in micro-blogging

ACL ID S13-2075
Title Experiments with DBpedia, WordNet and SentiWordNet as resources for sentiment analysis in micro-blogging
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

Sentiment Analysis in Twitter has become an important task due to the huge user-generated content published over such media. Such analysis could be useful for many domains such as Marketing, Finance, Politics, and So- cial. We propose to use many features in order to improve a trained classifier of Twitter mes- sages; these features extend the feature vector of uni-gram model by the concepts extracted from DBpedia, the verb groups and the similar adjectives extracted from WordNet, the Senti- features extracted using SentiWordNet and some useful domain specific features. We also built a dictionary for emotion icons, abbrevia- tion and slang words in tweets which is useful before extending the tweets with different fea- tures. Adding these features has improved the f-measure accu...