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 Social. We propose to use many features in order to improve a trained classifier of Twitter messages; 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 Sentifeatures extracted using SentiWordNet and some useful domain specific features. We also built a dictionary for emotion icons, abbreviation and slang words in tweets which is useful before extending the tweets with different features. Adding these features has improved the f-measure accuracy 2% with SVM and 4% with NaiveBayes.
Authors in REF used the concepts extracted from DBPedia and the adjectives from WordNet, they reported that the DBpedia concepts are useful with Naïve-Bayes classifier but less useful with SVM.
Experiments with DBpedia, WordNet and SentiWordNet as resources for sentiment analysis in micro-blogging
*SEM
Computer Science