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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Modelling Public Sentiment in Twitter: Using Linguistic Patterns to Enhance Supervised Learning
verfasst von : Prerna Chikersal, Soujanya Poria, Erik Cambria, Alexander Gelbukh, Chng Eng Siong
Erschienen in: Computational Linguistics and Intelligent Text Processing
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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.