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

2017-08-01 · SEMEVAL 2017 8 · Yunxiao Zhou, Man Lan, Yuanbin Wu

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.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

BIG-bench Machine Learning Feature Engineering General ClassificationLemmatizationSentiment AnalysisWord Embeddings

ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection

2018-06-01 · SEMEVAL 2018 6 · Yunxiao Zhou, Man Lan, Yuanbin Wu

This paper describes the system we submitted to Task 10 (Capturing Discriminative Attributes) in SemEval 2018. Given a triple (word1, word2, attribute), this task is to predict whether it exemplifies a semantic differenc…

Attribute BIG-bench Machine Learning Feature Engineering Machine Translation+2

ECNU at SemEval-2016 Task 5: Extracting Effective Features from Relevant Fragments in Sentence for Aspect-Based Sentiment Analysis in Reviews

2016-06-01 · SEMEVAL 2016 6 · Mengxiao Jiang, Zhihua Zhang, Man Lan

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA)Sentence Sentiment Analysis

ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models

2018-06-01 · SEMEVAL 2018 6 · Huimin Xu, Man Lan, Yuanbin Wu

This paper describes our submissions to SemEval 2018 task 1. The task is affect intensity prediction in tweets, including five subtasks. We participated in all subtasks of English tweets. We extracted several traditional…

BIG-bench Machine Learning Emotion Classification Feature Engineering General Classification+2

ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter

2016-06-01 · SEMEVAL 2016 6 · Yunxiao Zhou, Zhihua Zhang, Man Lan

Feature Engineering Language Modeling Language Modelling Sentence+1

ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain

2017-08-01 · SEMEVAL 2017 8 · Mengxiao Jiang, Man Lan, Yuanbin Wu

This paper describes our systems submitted to the Fine-Grained Sentiment Analysis on Financial Microblogs and News task (i.e., Task 5) in SemEval-2017. This task includes two subtasks in microblogs and news headline doma…

Feature Engineering regression Sentiment Analysis