Akshatha, M and Lavanya, MC and Seema, HR and Shashank, S (2024) A Machine LearningBased Optimization Approach to Analyze the Text-Based Reviews for Improving Graduation Rates for Cloud-Based Architectures. [Book Chapter]
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The world of social media has evolved into a crucial forum for individuals to share their ideas and opinions because of the quick development of mobile technology. Businesses and political organizations can make strategic choices with the aid of public opinion research. Considering this, the importance of sentiment analysis in determining the polarity of public opinion cannot be overstated. Many social media analysis studies categorize sentiment into three groups: neutral, negative, and positive. This study is based on a word cloud model that has already been trained and is being fine-tuned. The suggested approach aims to increase case reviews using text accuracy. Text reviews are typically neglected since they are difficult to categorize into any of the classes and are thus ignored. With the help of this study, we will be able to categorize these reviews into any of the classes and so enhance the analysis.
| Item Type: | Book Chapter |
|---|---|
| Publication: | Reliable and Intelligent Optimization in Multi-Layered Cloud Computing Architectures |
| Publisher: | CRC Press |
| Additional Information: | The copyright for this article belongs to CRC Press. |
| Department/Centre: | Division of Interdisciplinary Sciences > Robert Bosch Centre for Cyber Physical Systems |
| Date Deposited: | 25 Sep 2024 05:16 |
| Last Modified: | 25 Sep 2024 05:16 |
| URI: | http://eprints.iisc.ac.in/id/eprint/85223 |
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