Sentimental Classification of News Headlines using Recurrent Neural Network
S. Prakashini1, D. Vijayakumar2
1S.Prakashini*, Computer Science and Engineering, National Engineering College, Kovilpatti, India.
2D.Vijayakumar, Computer Science, and Engineering, National Engineering College, Kovilpatti, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on April 01, 2020. | Manuscript published on April 10, 2020. | PP: 207-210 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3573049620/2020©BEIESP | DOI: 10.35940/ijitee.F3573.049620
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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: 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.
Keywords: Classification, Clustering, News headlines, Recurrent Neural Network.
Scope of the Article: Clustering