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.

Exploring Diverse Features for Sentiment Quantification Using Machine Learning Algorithms

“…In SemEval-2017, task 4 is again divided into five tasks where task D and task E is binary and ordinal quantification of data respectively. Sentiment quantification has also been applied on content other than English, for instance, the research study [29] uses Arabic language content. A team named NRU-HSE [28] used LSTM for classification of data for task D and the results were compared with that was proposed in 2016 in terms of KLD.…”

“…A team named NRU-HSE [28] used LSTM for classification of data for task D and the results were compared with that was proposed in 2016 in terms of KLD. Another team named TwiSE [29] also participated in SemEval-2017. Logistic Regression is used as base classifier with Classify and Count algorithm and promising results were observed.…”