Visual Social Data Clustersfor Effective Topics Tendnecy with Hybrid Machine Learning Techniques
Upendar Penmetcha1, K. Rajendra Prasad2
1Upendar Penmetcha, Dept. of CSE, Mahatma Gandhi Institute of Technology, Hyderabad, India.
2Dr. K Rajendra Prasad*, Professor and Head, Dept. of CSE, Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 4100-4104 | Volume-8 Issue-5, January 2020. | Retrieval Number: D4871118419/2020©BEIESP | DOI: 10.35940/ijrte.D4871.018520
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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: The machine learning is an emerging field in social classification of data, which enable the learning of social data patterns and classify the data by unsupervised approaches. Majorly, k-means and graph-based machine learning algorithms are used for discovering of social data clusters based on similarity features of user views, opinions. This paper presents the sentimental analysis of social users for the topics using the cluster tendency of derived clusters. The experimental of social data clusters and the cluster tendency are visualized for effective sentiment of topics analysis.
Keywords: Machine Learning, Classification, Cluster tendency, Social Data Clusters, Sentiment Analysis.
Scope of the Article: Machine Learning.