ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification

2017-08-01SEMEVAL 2017Unverified0· sign in to hype

Yunxiao Zhou, Man Lan, Yuanbin Wu

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Abstract

This paper reports our submission to subtask A of task 4 (Sentiment Analysis in Twitter, SAT) in SemEval 2017, i.e., Message Polarity Classification. We investigated several traditional Natural Language Processing (NLP) features, domain specific features and word embedding features together with supervised machine learning methods to address this task. Officially released results showed that our system ranked above average.

Tasks

BIG-bench Machine Learning Feature Engineering General Classification Lemmatization Sentiment Analysis Word Embeddings

ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification

Abstract

Tasks

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