Ensemble Feature Selection to improve the classifier Performance in Sentimental Analysis
DOI:
https://doi.org/10.61841/ywfmjm18Keywords:
Sentimental Analysis, Word Embedding Dimensionality Reduction, Visualization, ToxicityAbstract
Pre-trained word embedding’s are used in several downstream applications as well as for constructing representations for sentences, paragraphs, and documents. One improvement area is reducing the dimensionality of word embedding. Reducing the size of word embedding can improve their utility in memory-constrained devices, benefiting several real-world applications. Therefore, in this paper, we focus on how to classify textual information, which consists of online comments from Wikipedia talk page edits where unsupervised learning approaches are used to obtain better performance of sentimental analysis. To this end, we first analyze the dataset for pre-training by using a phenomenon called glove word embedding and giving some unique dimensions to each comment. Then we reduce the dimensions of the comments using the dimensionality reduction approach and propose an iterative algorithm called t-SNE to visualize the high-dimensional data. Finally, a bidirectional LSTM model is built using keras to classify the sentences into appropriate types of toxicity. To the best of our knowledge, this work is first to the study of negative online behaviors, like various types of toxic comments.
Downloads
References
1. Revati Sharma, Meetkumar Patel.September 2018. Toxic Comment Classification Using Neural Networks
and Machine Learning: International Advanced Research Journal in Science, Engineering and Technology
5,9
2. Betty van Aken, Julian Risch, Ralf Krestel, and Alexander Löser. 2017. Challenges for Toxic Comment
Classification: An In-Depth Error Analysis.
3. Mujahed A. Saif, Alexander N. Medvedev, Maxim A. Medvedev, and Todorka Atanasova. 11 December
2018. Classification of Online Toxic Comments Using the Logistic Regression and Neural Networks
Models: AIP Conference Proceedings 2048.
4. Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Aristidis G. Vrahatis, and Vassilis P. Plagianakos. 2018.
Convolutional neural networks for toxic comment classification. In SETN ’18: 10th Hellenic Conference on Artificial Intelligence, July 9–12, 2018, Patras, Greece. ACM, New York, NY, USA.
5. Björn Gambäck and Utpal Kumar Sikdar. 2017. Using convolutional neural networks to classify hate speech. In ALW1@ACL.
6. Anna Schmidt and Michael Wiegand. 2017. A survey on hate speech detection using natural language processing. In SocialNLP@EACL.
7. Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts Xingyou Wang1, Weijie Jiang2, and Zhiyong Luo3. http://www.aclweb.org/anthology/C16-1229
Downloads
Published
Issue
Section
License
Copyright (c) 2020 AUTHOR
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
