Lexical and Learning-based Emotion Mining from Text
- Ameneh Gholipour Shahraki
- Osmar R. Zaiane, University of Alberta (Database)
Emotion mining from text refers to the detection of people’s emotions based on observations of their writings. 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 targeted emotions, then, we propose several lexical and learning based methods to classify the emotion of test tweets and study the effect of different feature sets. 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. In addition we test our approach on other datasets, Twitter, and formally written texts and show that our approach achieves higher accuracy, compared with state-of-the-art methods working on these corpora.
Citation
A. Shahraki, O. Zaiane. "Lexical and Learning-based Emotion Mining from Text". International Conference on Intelligent Text Processing and Computational Linguistics (CICLing), Budapest, Hungary, April 2017.| Keywords: | |
| Category: | In Conference |
| Web Links: | Webdocs |
BibTeX
@incollection{Shahraki+Zaiane:CICLing17,
author = {Ameneh Gholipour Shahraki and Osmar R. Zaiane},
title = {Lexical and Learning-based Emotion Mining from Text},
booktitle = {International Conference on Intelligent Text Processing and
Computational Linguistics (CICLing)},
year = 2017,
}Last Updated: November 04, 2019Submitted by Sabina P
