Tweester at SemEval-2016 task 4: Sentiment analysis in twitter using semantic-affective model adaptation

Abstract

We describe our submission to SemEval2016 Task 4: Sentiment Analysis in Twitter. The proposed system ranked first for the subtask B. Our system comprises of multiple independent models such as neural networks, semantic-affective models and topic modeling that are combined in a probabilistic way. The novelty of the system is the employment of a topic modeling approach in order to adapt the semantic-affective space for each tweet. In addition, significant enhancements were made in the main system dealing with the data preprocessing and feature extraction including the employment of word embeddings. Each model is used to predict a tweet's sentiment (positive, negative or neutral) and a late fusion scheme is adopted for the final decision.