Abstract
Sentiment analysis is to abstract sentiment polarity, positive or negtive from text. Nowadays reasearchers have proposed many models. However, the existing researches ignore the potential of ensemble model in sentiment analysis, and don't consider the application of ensemble model in sentiment analysis. This paper proposes an ensemble model for sentiment analysis, which firstly uses the word2vec model to convert the text into a matrix, then uses CNN to extract local features, and combine with LSTM to improve the accuracy of the analysis. Experimental results on the bechmark dataset show that our proposed method delivers significant improvements compared with the baselines on the task of sentiment analysis.
References
[1]
Rodrigo Sandoval-Almazan and David Valle-Cruz. 2020. Sentiment Analysis of Facebook Users Reacting to Political Campaign Posts. Digit. Gov.: Res. Pract. 1, 2, Article 12 (April 2020), 13 pages.
[2]
Peng Liu, Tingwen Liu, Jinqiao Shi, Xuebin Wang, Zelin Yin, and Can Zhao. 2020. Aspect level sentiment classification with unbiased attention and target enhanced representations. In Proceedings of the 35th Annual ACM Symposium on Applied Computing. Association for Computing Machinery, New York, NY, USA, 843--850.
[3]
Chuhan Wu, Fangzhao Wu, Junxin Liu, Yongfeng Huang, and Xing Xie. 2019. Sentiment Lexicon Enhanced Neural Sentiment Classification. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, New York, NY, USA, 1091--1100.
[4]
Chengzhi Jiang and Tadahiko Kumamoto. 2019. Method for Computing Emotions of Tweets with an Emoticon. In Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services. Association for Computing Machinery, New York, NY, USA, 193--201.
[5]
Honglei Zhuang, Fang Guo, Chao Zhang, Liyuan Liu, and Jiawei Han. 2020. Joint Aspect-Sentiment Analysis with Minimal User Guidance. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, 1241--1250.
[6]
Yinxia Lou, Yue Zhang, Fei Li, Tao Qian, and Donghong Ji. 2020. Emoji-Based Sentiment Analysis Using Attention Networks. ACM Trans. Asian Low-Resour. Lang. Inf. Process.19, 5, Article 64 (August 2020), 13 pages.
[7]
Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. 2014. SemEval-2014 Task 4: Aspect Based Sentiment Analysis. In SemEval@COLING. The Association for Computer Linguistics, 27--35.
[8]
Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Suresh Manandhar, and Ion Androutsopoulos. 2015. SemEval-2015 Task 12: Aspect Based Sentiment Analysis. In SemEval@NAACL-HLT. The Association for Computer Linguistics, 486--495.
[9]
Michele Corazza, Stefano Menini, Elena Cabrio, Sara Tonelli, and Serena Villata. 2020. A Multilingual Evaluation for Online Hate Speech Detection. ACM Trans. Internet Technol. 20, 2, Article 10 (May 2020), 22 pages.
[10]
Samar Al-Saqqa and Arafat Awajan. 2019. The Use of Word2vec Model in Sentiment Analysis: A Survey. In Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control (AIRC '19). Association for Computing Machinery, New York, NY, USA, 39--43.
[11]
https://blog.csdn.net/qq_24003917/article/details/80389976
[12]
Huanhuan Yuan, Yongli Wang, Xia Feng, and Shurong Sun. 2018. Sentiment Analysis Based on Weighted Word2vec and Att-LSTM. In Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence (CSAI '18). Association for Computing Machinery, New York, NY, USA, 420--424.
[13]
Yuxiao Chen, Jianbo Yuan, Quanzeng You, and Jiebo Luo. 2018. Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM. In Proceedings of the 26th ACM international conference on Multimedia (MM '18). Association for Computing Machinery, New York, NY, USA, 117--125.
[14]
Borun Xu, Xiaoxiao Wang, Bo Yang, and Zhongfeng Kang. 2020. Target Embedding and Position Attention with LSTM for Aspect Based Sentiment Analysis. In Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence. Association for Computing Machinery, New York, NY, USA, 93--97.
Index Terms
- An Ensemble Model for Sentiment Analysis
Recommendations
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 ...
Domain-specific sentiment analysis using contextual feature generation
TSA '09: Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinionThis paper presents a novel framework for sentiment analysis, which exploits sentiment topic information for generating context-driven features. Since the domain-specific nature of sentiment classification led the task more problematic, considering more ...
Sentiment classification: a combination of PMI, sentiwordnet and fuzzy function
ICCCI'12: Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part IIDiscerning a consensus opinion about a product or service is difficult due to the many opinions on the web. To overcome this problem, sentiment classification has been applied as an important approach for evaluation in sentiment mining. Recently, ...
Comments
Information & Contributors
Information
Published In
December 2020
687 pages
ISBN:9781450388665
DOI:10.1145/3452940
Copyright © 2020 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 ACM 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 May 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICITEE2020
ICITEE2020: The 3rd International Conference on Information Technologies and Electrical Engineering
December 3 - 5, 2020
Hunan, Changde City, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 42Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)0
Reflects downloads up to 28 Feb 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 in