Sentiment analysis of hot event comments based on deep learning
Authors: 
Zelong Yin, Jinyan Zhou, Lianjun Yu, Yang Jiao, Yang Liu, Tian Zhang, Guogang ZhaoAuthors Info & Claims
Zelong Yin, Jinyan Zhou, Lianjun Yu, Yang Jiao, Yang Liu, Tian Zhang, Guogang ZhaoAuthors Info & ClaimsPages 570 - 573
Abstract
Comments on Sina Weibo are one of the main ways for users to interact and give feedback when viewing Weibo content. Some comments may use emotional language to guide public emotions and attitudes, and gather together to form an atmosphere of public opinion. In recent years, deep learning has made great progress and has been widely used in natural language processing to solve problems such as sentiment analysis, question and answer systems, and speech recognition. This article takes the recent ChatGPT popular out-of-circle event as an example. It uses Python to crawl the comment data in related Weibo, and analyzes netizens' emotional attitude towards the event and its positive and negative classification based on the BiLSTM model. Realize the emotional analysis of netizens on the event so as to grasp the public opinion situation of the network in a timely manner.
References
[1]
Wang Y, Huang X, Li B, Spreading mechanism of Weibo public opinion phonetic representation based on the epidemic model[J]. International Journal of Speech Technology, 2021, 26(1).
[2]
Mayukh S, Ilanthenral K, Vasantha K . Deep Learning for predicting neutralities in Offensive Language Identification Dataset[J]. Expert Systems With Applications, 2021, 185.
[3]
A. B, M. R, M. J, Photovoltaic generation forecasting using convolutional and recurrent neural networks[J]. Energy Reports, 2023, 9(S12).
[4]
Tonglin F, Xinrong L . Hybrid the long short-term memory with whale optimization algorithm and variational mode decomposition for monthly evapotranspiration estimation[J]. Scientific Reports, 2022, 12(1).
[5]
Hakim A E K, El F M E H, Korashy H M . Deep learning for emotion analysis in Arabic tweets[J]. Journal of Big Data, 2021, 8(1).
[6]
Choudrie J, Patil S, Kotecha K, Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study[J]. Information Systems Frontiers, 2021, 23(6).
[7]
Wu Kuihua, Wu Jian, Feng Liang, Yang Bo, Liang Rong, Yang Shenquan, Zhao Ren. An attention‐based CNN‐LSTM‐BiLSTM model for short‐term electric load forecasting in integrated energy system[J]. International Transactions on Electrical Energy Systems, 2020, 31(1).
[8]
Zhu Junmin, Gao Yiang. Application Research of Deep Learning in Natural Language Processing [C]. Computer Science and Adcanced Materials, 2019 (4)
Index Terms
- Sentiment analysis of hot event comments based on deep learning
Recommendations
Does sentiment help requirement engineering: exploring sentiments in user comments to discover informative comments
AbstractUser comments are valuable resources for software improvement; however, owing to excessive volume, informative comments need to be selectively analyzed. We attempt to address this problem by sentiment analysis and expect sentiment can be a useful ...
Joint sentiment/topic model for sentiment analysis
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge managementSentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet ...
Topic sentiment change analysis
MLDM'11: Proceedings of the 7th international conference on Machine learning and data mining in pattern recognitionPublic opinions on a topic may change over time. Topic Sentiment change analysis is a new research problem consisting of two main components: (a) mining opinions on a certain topic, and (b) detect significant changes of sentiment of the opinions on the ...
Comments
Information & Contributors
Information
Published In
October 2023
1809 pages
ISBN:9798400708305
DOI:10.1145/3650400
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 April 2024
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
EITCE 2023
EITCE 2023: 2023 7th International Conference on Electronic Information Technology and Computer Engineering
October 20 - 22, 2023
Xiamen, China
Acceptance Rates
Overall Acceptance Rate 508 of 972 submissions, 52%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 21Total Downloads
- Downloads (Last 12 months)21
- Downloads (Last 6 weeks)5
Reflects downloads up to 08 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format