Polarity detection on real-time news data using opinion mining

Title

Polarity detection on real-time news data using opinion mining

Creator

Prathap B.R.; Sujatha A.K.; Yadav C.B.S.; Mounika M.

Description

Sentimental Analysis or Opinion Mining plays a vital role in the experimentation field that determines the users opinions, emotions and sentiments concealing a text. News on the Internet is becoming vast, and it is drawing attention and has reached the point of adequately affecting political and social realities. The popular way of checking online content, i.e. manual knowledge-based on the facts, is practically impossible because of the enormous amount of data that has now generated online. The issue can address by using Machine Learning Algorithms and Artificial Intelligence. One of the Machine Learning techniques used in this is Naive Bayes classifier. In this paper, the polarity of the news article determined whether the given news article is a positive, negative or neutral Naive Bayes Classifier, which works well with NLP (Natural Language problems) used for many purposes. It is a family of probabilistic algorithms that used to identify a word from a given text. In this, we calculate the probability of each word in a given text. Using Bayes theorem, they are getting the probabilities based on the given conditions. Topic Modeling is analytical modelling for finding the abstract of topics from a cluster of documents. Latent Dirichlet Allocation (LDA) is a topic model is used to classify the text in a given document to a specified topic. The news article is classified as positive or negative or neutral using Naive Bayes classifier by calculating the probabilities of each word from a given news article. By using topic modelling (LDA), topics of articles are detected and record data separately. The calculation of the overall sentiment of a chosen topic from different newspapers from previously recorded data done. 2020 The authors and IOS Press.

Source

Advances in Parallel Computing, Vol-37, pp. 90-97.

Date

2020-01-01

Publisher

IOS Press BV

Subject

Fake news detection; LDA; Polarity detection; Sentiment analysis

Coverage

Prathap B.R., CHRIST (Deemed to be University), Bangalore, Karnataka, India; Sujatha A.K., CHRIST (Deemed to be University), Bangalore, Karnataka, India; Yadav C.B.S., CHRIST (Deemed to be University), Bangalore, Karnataka, India; Mounika M., CHRIST (Deemed to be University), Bangalore, Karnataka, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 9275452

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.3233/APC200124

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096670392&doi=10.3233%2fAPC200124&partnerID=40&md5=8801a83d06ee59a2cc09347fd68fcce2