Supervised Sentiment Analysis Algorithms
Main Article Content
Abstract
Sentiment analysis is used to analyse customer sentiment by the process of using natural
language processing, text analysis, and statistics. A good customer survey understands the
sentiment of their customers—what, how and why they’re saying it. Sentiment dataset can be
found mainly in tweets, comments and reviews. Sentiment Analysis understands emotions
with the help of software, and it is playing an inevitable role in today’s workplaces.
Sentiment analysis for opinion mining has become an emerging area where more research
and innovations are done. Sentiment or opinion analysis based on a domain is done using
several algorithms. Machine learning is a concept among this area. In this, the main focus is
on the supervised sentiment analysis or opinion mining algorithms. Supervised learning is a
division coming under machine learning. Different methods of supervised learning and
sentiment analysis algorithms are considered and their mode of functioning is studied. Main
focus of this paper is on the recent trends of research and studies for sentiment classification,
taking into consideration the accuracy of different algorithmic techniques that can be
implemented for accurate prediction in sentiment Analysis.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
