Open AccessPerformance based Machine Learning Algorithm for Topic Oriented Text Categorization
Author(s) -
P. Ramya,
Vinod Kumar
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1429.0982s1119
Subject(s) - sentiment analysis , categorization , computer science , support vector machine , naive bayes classifier , precision and recall , set (abstract data type) , newspaper , recall , machine learning , entropy (arrow of time) , artificial intelligence , data mining , psychology , advertising , physics , quantum mechanics , business , cognitive psychology , programming language
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.