@inproceedings{42b4fa33643a4a47badc0425ddb02e12,
title = "Modelling public sentiment in twitter: Using linguistic patterns to enhance supervised learning",
abstract = "This paper describes a Twitter sentiment analysis system that classifies a tweet as positive or negative based on its overall tweet-level polarity. Supervised learning classifiers often misclassify tweets containing conjunctions such as “but” and conditionals such as “if”, due to their special linguistic characteristics. These classifiers also assign a decision score very close to the decision boundary for a large number tweets, which suggests that they are simply unsure instead of being completely wrong about these tweets. To counter these two challenges, this paper proposes a system that enhances supervised learning for polarity classification by leveraging on linguistic rules and sentic computing resources. The proposed method is evaluated on two publicly available Twitter corpora to illustrate its effectiveness.",
keywords = "Opinion Mining, Sentic Computing, Sentiment Analysis",
author = "Prerna Chikersal and Soujanya Poria and Erik Cambria and Alexander Gelbukh and Siong, {Chng Eng}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 16th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2015 ; Conference date: 14-04-2015 Through 20-04-2015",
year = "2015",
doi = "10.1007/978-3-319-18117-2_4",
language = "Ingl{\'e}s",
isbn = "9783319181165",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "49--65",
editor = "Alexander Gelbukh",
booktitle = "Computational Linguistics and Intelligent Text Processing - 16th International Conference, CICLing 2015, Proceedings",
address = "Alemania",
}