Detection of Criticism and Hate speech Text Formulation on Online Social Network Twitter for Semantic Recommendation System Framework
- DOI
- 10.2991/aer.k.210810.051How to use a DOI?
- Keywords
- Text Formulation, Criticism, Hate Speech, Bage of Word, Convolutional Neural Network, Recommendation System
- Abstract
User opinions on high-volume social media and various themes provide relevant information for sentiment analysis. This information can be collected and analyzed using a natural language processing with a monitoring system to support classification of criticism and hate speech. Regarding monitoring results, a knowledge-based recommendation system with sentiment analysis is supported to send messages to user in order to use positive sentences are not offensive, polite, wise and motivational for users with hateful attitudes. It is important to formulate sentences that can differentiate between criticism and hate speech. By compiling a formula sentence as a classification reference for the text obtained in a twitter tweet whether as criticism or including hate speech. Detection of sentences containing criticism and hate speech using Bag of Word and Convolutional Neural Network to detect hate speech dan criticism sentence via Twitter. The detection results are used for the semantic recommendation system framework that includes sentiment analysis and classification of hate speech.
- Copyright
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Migunani AU - Adi Setiawan AU - Irwan Sembiring PY - 2021 DA - 2021/08/11 TI - Detection of Criticism and Hate speech Text Formulation on Online Social Network Twitter for Semantic Recommendation System Framework BT - Proceedings of the 2nd Borobudur International Symposium on Science and Technology (BIS-STE 2020) PB - Atlantis Press SP - 294 EP - 301 SN - 2352-5401 UR - https://doi.org/10.2991/aer.k.210810.051 DO - 10.2991/aer.k.210810.051 ID - 2021 ER -