TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification

2017-08-01 · SEMEVAL 2017 8 · Georgios Balikas

The paper describes the participation of the team {`}TwiSE{''} in the SemEval-2017 challenge. Specifically, I participated at Task 4 entitled {`}Sentiment Analysis in Twitter{''} for which I implemented systems for five-point tweet classification (Subtask C) and five-point tweet quantification (Subtask E) for English tweets. In the feature extraction steps the systems rely on the vector space model, morpho-syntactic analysis of the tweets and several sentiment lexicons. The classification step of Subtask C uses a Logistic Regression trained with the one-versus-rest approach. Another instance of Logistic Regression combined with the classify-and-count approach is trained for the quantification task of Subtask E. In the official leaderboard the system is ranked \textit{5/15} in Subtask C and \textit{2/12} in Subtask E.

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General Classification Opinion Mining regression Sentiment AnalysisSentiment ClassificationTwitter Sentiment AnalysisWord Embeddings

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