Sentimental Classification of News Headlines using Recurrent Neural Network

Sentiment analysis combines the natural language processing task and analysis of the text that attempts to predict the sentiment of the text in terms of positive and negative comments. Nowadays, the tremendous volume of news originated via different webpages, and it is feasible to determine the opin...

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Bibliographic Details
Published in:International journal of innovative technology and exploring engineering 2020-04, Vol.9 (6), p.207-210
Main Authors: Prakashini, S., kumar, D.Vijaya
Format: Article
Language:English
Online Access:Get full text
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Summary:Sentiment analysis combines the natural language processing task and analysis of the text that attempts to predict the sentiment of the text in terms of positive and negative comments. Nowadays, the tremendous volume of news originated via different webpages, and it is feasible to determine the opinion of particular news. This work tries to judge completely various machine learning techniques to classify the view of the news headlines. In this project, propose the appliance of Recurrent Neural Network with Long Short Term Memory Unit(LSTM), focus on seeking out similar news headlines, and predict the opinion of news headlines from numerous sources. The main objective is to classify the sentiment of news headlines from various sources using a recurrent neural network. Interestingly, the proposed attention mechanism performs better than the more complex attention mechanism on a held-out set of articles.
ISSN:2278-3075
2278-3075
DOI:10.35940/ijitee.F3573.049620