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Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation
2016
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)
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
doi:10.18653/v1/s16-1023
dblp:conf/semeval/PalogiannidiKCK16
fatcat:k6fk62sqvzcp5lo72qvrxvhg4e