Performance based Machine Learning Algorithm for Topic Oriented Text Categorization
Paruchuri Ramya1, Geetha Guttikonda2, Vemuri Sindhura3, Vinod Kumar Gadde4
1Paruchuri Ramya, Project Associate, CTS, Department of Information Technology, VR Siddhartha Engineering College, Kanuru, Vijayawada, Chennai (Tamil Nadu), India.
2Geetha Guttikonda, Assistant Professor, V R Siddhartha Engineering College Greater Vijayawada (Andhra Pradesh), India.
3Vemuri Sindhura, Department of Marketing and Communications, Telangana Innovation Cell (TSIC) Hyderabad (Telangana), India.
4Vinod Kumar Gadde, JKC College, Guntur University, India.
Manuscript received on 19 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 02 November 2019 | PP: 3501-3506 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B14290982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1429.0982S1119
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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: With the growth of societal news on the web, public opinions are given major importance in decision-making. Researchers of text-based mining have made number of evaluations and were diversified using different data mining methods so as to make the conclusions positive, negative and neutral. So, opinions of people are considered to mine the social information as people give superfluous interest to the reports. In this paper the newspaper data set is considered to find the opinion mining to evaluate the sentiment. Sentiment Analysis is used to compute the opinions of people before they judge on a particular issue. Machine Learning is one of the important approaches for analysis of sentiments. Different methods like Naïve Bayes, SVM, Maximum entropy and SLDA are used for classifying the sentiments. Predictions based on precision, f-measure, recall are done to determine which method best suits the classification.
Keywords: Opinion, Sentiment Analysis, Machine Learning, Naive Bayes, SVM, Maximum Entropy, SLDA, Prediction, Precision, Recall, F-Measure.
Scope of the Article: Machine Learning