SentiRich: Sentiment Analysis of Tweets Based on a Rich Set of Features
Jabreel Mohammed, Antonio Moreno · Frontiers in artificial intelligence and applications · 2016
This paper proposes the representation of tweets using a novel set of features, which include a bag of negated words and the information provided by seven lexicons. The polarity of tweets is determined by a classifier based on a Support Vector Machine. The system has been evaluated on the standard tweet sets used in the SemEval 2015 competition, obtaining results that, in most cases, outperform those of the state-of-the-art sentiment analysis systems.