@inproceedings{460110c5ab7a44aca3eab8e47865926a,
title = "ECNU: Expression- And Message-level Sentiment Orientation Classification in Twitter Using Multiple Effective Features",
abstract = "Microblogging websites (such as Twitter, Facebook) are rich sources of data for opinion mining and sentiment analysis. In this paper, we describe our approaches used for sentiment analysis in twitter (task 9) organized in SemEval 2014. This task tries to determine whether the sentiment orientations conveyed by the whole tweets or pieces of tweets are positive, negative or neutral. To solve this problem, we extracted several simple and basic features considering the following aspects: surface text, syntax, sentiment score and twitter characteristic. Then we exploited these features to build a classifier using SVM algorithm. Despite the simplicity of features, our systems rank above the average.",
author = "Jiang Zhao and Man Lan and Zhu, \{Tian Tian\}",
note = "Publisher Copyright: {\textcopyright} 8th International Workshop on Semantic Evaluation, SemEval 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014, Proceedings. All rights reserved.; 8th International Workshop on Semantic Evaluation, SemEval 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014 ; Conference date: 23-08-2014 Through 24-08-2014",
year = "2014",
doi = "10.3115/v1/s14-2042",
language = "英语",
series = "8th International Workshop on Semantic Evaluation, SemEval 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014, Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "259--264",
editor = "Preslav Nakov and Torsten Zesch",
booktitle = "8th International Workshop on Semantic Evaluation, SemEval 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014, Proceedings",
address = "澳大利亚",
}