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

Abstract

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 5/15 in Subtask C and 2/12 in Subtask E.

Tasks

Reproductions