Multi-Layer Classification and Segmentation with Opinion Mining Learning Ensemble
Ravi Kumar Kuchipudi1, V.V. Jaya Rama Krishnaiah2
1Ravi Kumar Kuchipudi*, Research Scholar, Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur (Andhra Pradesh), India.
2Dr. V.V. Jaya Rama Krishnaiah, Associate Professor, ASN Engineering College, Tenali (Andhra Pradesh), India.
Manuscript received on October 18, 2021. | Revised Manuscript received on October 27, 2021. | Manuscript published on October 30, 2021. | PP: 276-282 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A31371011121 | DOI: 10.35940/ijeat.A3137.1011121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Social media has become the main area for advertising, events, campaigns, protests etc. in recent years. It offers the public a forum for expressing their opinions and beliefs to the masses. The user beliefs, habits and interests of businesses are extremely important and give users a glimpse into their thinking. Data mining is one of the tools that enables these businesses to extract useful information from user data that can be examined to generate a set of knowledge and identify a user opinion that helps companies to create user-specific products. Twitter Data Mining and other social platforms are very important since its enormous user base consists of a mixture of thoughts and opinions that may be used to anticipate results of campaigns, product assessments and similarity if correctly studied. This research provides a classification system to perform Twitter Opinion Mining Segmentation based on Ensemble Learning. The suggested approach can detect and filter out boxes and uses text segmentation for efficient text classification and voice taging.
Keywords: Machine Learning, Supervised Learning, Text Analysis, Sentiment Analysis, Natural Language Processing.
Scope of the Article: Machine Learning