SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning Article Swipe

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Prerna Chikersal , Soujanya Poria , Erik Cambria ·

YOU? · · 2015 · Open Access · · DOI: https://doi.org/10.18653/v1/s15-2108

We describe a Twitter sentiment analysis system developed by combining a rule-based classifier with supervised learning.We submitted our results for the message-level subtask in SemEval 2015 Task 10, and achieved a F 1 -score of 57.06%.The rule-based classifier is based on rules that are dependent on the occurrences of emoticons and opinion words in tweets.Whereas, the Support Vector Machine (SVM) is trained on semantic, dependency, and sentiment lexicon based features.The tweets are classified as positive, negative or unknown by the rule-based classifier, and as positive, negative or neutral by the SVM.The results we obtained show that rules can help refine the SVM's predictions.

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Computer Science

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Support Vector Machine

Machine Learning

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Computer science Sentiment analysis Artificial intelligence Support vector machine Lexicon Classifier (UML) SemEval Natural language processing Rule-based system Machine learning Task (project management) Management Economics

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Type: article
Language: en
Landing Page: https://doi.org/10.18653/v1/s15-2108
PDF: https://www.aclweb.org/anthology/S15-2108.pdf
OA Status: gold
Cited By: 116
References: 21
Related Works: 10
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DOI: https://doi.org/10.18653/v1/s15-2108

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Title: SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning

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Language: en

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Publication year: 2015

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Authors: Prerna Chikersal, Soujanya Poria, Erik Cambria

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Landing page: https://doi.org/10.18653/v1/s15-2108

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Concepts: Computer science, Sentiment analysis, Artificial intelligence, Support vector machine, Lexicon, Classifier (UML), SemEval, Natural language processing, Rule-based system, Machine learning, Task (project management), Management, Economics

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Cited by: 116

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