Statistical Exploration and Sentiment Analysis of WhatsApp Chats using Supervised Learning
Article No.: 97, Pages 1 - 6
Abstract
This paper presents the sentiment analysis of WhatsApp chat data while also evaluating user activity, employing VADER and supervised learning. The study encompasses both sentiment classification utilizing VADER's lexicon-based approach and supervised sentiment analysis using the Naive Bayes and SVM model. The research extends to statistical exploration like identifying the most active user within the group chat dataset and other statistical information. By contrasting the efficiency and accuracy of these techniques, the paper aids in method selection based on specific analysis goals. Results offer insights into sentiment trends and user engagement for informed decision-making.
References
[1]
Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
[2]
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in information retrieval, 2(1–2), 1-135.
[3]
Yoon, J., & Kim, H. (2017, November). Multi-channel lexicon integrated CNN-BiLSTM models for sentiment analysis. In Proceedings of the 29th conference on computational linguistics and speech processing (ROCLING 2017) (pp. 244-253).
[4]
Hutto, C., & Gilbert, E. (2014, May). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the international AAAI conference on web and social media (Vol. 8, No. 1, pp. 216-225).
[5]
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R. R., & Le, Q. V. (2019). Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems, 32.
[6]
Chrikov, I., Soria, K. M., Horgos, B., & Jones-White, D. (2020). Undergraduate and graduate students’ mental health during the COVID-19 pandemic.
[7]
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1-2), 1-135.
[8]
Raj, S., & Raj, S. (2019). A Comparative Study of Naive Bayes Classifier for Sentiment Analysis. Procedia Computer Science, 132, 719-725.
[9]
Hutto, C. J., & Gilbert, E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. In Eighth International Conference on Weblogs and Social Media (ICWSM-14)444
[10]
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., & Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 conference on empirical methods in natural language processing (pp. 1631-1642).
[11]
Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253.
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 ...
A document-level sentiment analysis approach using artificial neural network and sentiment lexicons
The abundance of discussion forums, Weblogs, e-commerce portals, social networking, product review sites and content sharing sites has facilitated flow of ideas and expression of opinions. The user-generated text content on Internet and Web 2.0 social ...
Comments
Information & Contributors
Information
Published In
November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
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: 13 May 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 30Total Downloads
- Downloads (Last 12 months)30
- Downloads (Last 6 weeks)6
Reflects downloads up to 05 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