A Novel Auto Encoder- Network- Based Ensemble Technique for Sentiment Analysis Using Tweets on COVID- 19 Data
- Title
- A Novel Auto Encoder- Network- Based Ensemble Technique for Sentiment Analysis Using Tweets on COVID- 19 Data
- Creator
- Jyothsna R.; Rohini V.; Paulose J.
- Description
- The advances in digitalization have resulted in social media sites like Twitter and Facebook becoming very popular. People are able to express their opinions on any subject matter freely across the social media networking sites. Sentiment analysis, also termed emotion artificial intelligence or opinion mining, can be considered a technique for analyzing the mood of the general public on any subject matter. Twitter sentiment analysis can be carried out by considering tweets on any subject matter. The objective of this research is to implement a novel algorithm to classify the tweets as positive or negative, based on machine learning, deep learning, the nature inspired algorithm and artificial neural networks. The proposed novel algorithm is an ensemble of the decision tree algorithm, gradient boosting, Logistic Regression and a genetic algorithm based on the auto-encoder technique. The dataset under consideration is tweets on COVID-19 in May 2021. 2024 Taylor & Francis Group, LLC.
- Source
- Machine Intelligence: Computer Vision and Natural Language Processing, pp. 257-272.
- Date
- 2023-01-01
- Publisher
- CRC Press
- Coverage
- Jyothsna R., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Rohini V., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Paulose J., Department of Computer Science, Christ University, Karnataka, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-100096031-0; 978-103220199-3
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Jyothsna R.; Rohini V.; Paulose J., “A Novel Auto Encoder- Network- Based Ensemble Technique for Sentiment Analysis Using Tweets on COVID- 19 Data,” CHRIST (Deemed To Be University) Institutional Repository, accessed May 14, 2025, https://archives.christuniversity.in/items/show/18413.