Opinion mining on newspaper headlines using SVM and NLP
subject: CountVectorizer; Newspaper; NLTK; Opinion mining; Sentiment analysis; SGDClassifier; Stanford coreNLPm; SVM; Tf-idf
description: Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved.
creator: Rameshbhai C.J.; Paulose J.
source: International Journal of Electrical and Computer Engineering, Vol-9, No. 3, pp. 2152-2163.
publisher: Institute of Advanced Engineering and Science
date: 2019-01-01
rights: All Open Access; Gold Open Access; Green Open Access
relation: ISSN: 20888708
format: Online
language: English
type: Article
coverage: Rameshbhai C.J., Department of Computer Science, Christ University, Hosur Road, Bangalore, 560029, Karnataka, India; Paulose J., Department of Computer Science, Christ University, Hosur Road, Bangalore, 560029, Karnataka, India