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Emotion Mining from Text
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- Author / Creator
- Gholipour Shahraki, Ameneh
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Emotion mining is the science of detecting, analyzing, and evaluating humans’ feelings towards different events, issues, services, or any other interest. One of its specific directions is text emotion mining, that refers to analyzing people’s emotions based on observations of their writings. Text emotion mining is an interdisciplinary topic of interest and has many applications including helping customer care services, recommending music or movies to computer users, helping in selecting e-learning materials, filtering results of searches by emotion, and diagnosing depression or suicidal tendency.
In this work, we study the problem of text emotion classification. First, we collect and cleanse a corpus of Twitter messages that convey at least one of the emotions: anger, fear, joy, love, sadness, surprise, disgust, guilt, and thankfulness. Then, we propose several lexical and learning based methods to classify the emotion of test tweets and study the effect of different feature sets, dimension reduction techniques, different learning algorithms and configurations, and also try to address the problem of sparsity of the input data. Our experimental results show that a set of Naive Bayes classifiers, each corresponding to one emotion, using unigrams as features, is the best-performing method for the task. Moreover, we address the problem of multi-label emotion classification of texts, that is concerned with tweets that represent more than one emotion. In this case, again the Naive Bayes method outperforms the others.
In order to compare the efficiency of our algorithms, we test them also on a couple of other datasets, one of which is collected from Twitter, and the other contains a set of formally written texts. Our Naive Bayes approach achieves higher accuracy, compared with state-of-the-art methods working on these corpora. -
- Subjects / Keywords
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- Graduation date
- Fall 2015
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
- This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.