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Sentiment Classification of News Headlines on India in the US Newspaper: Semantic Orientation Approach vs Machine Learning

Chavan, Somanath S. (2018) Sentiment Classification of News Headlines on India in the US Newspaper: Semantic Orientation Approach vs Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

From the era of globalization every country is cautious about its image among other countrymen. In recent years, India is looking forward for good relationship with the USA. For measuring these relationships Indian government is aggressively trying to find new ways to understand 'how the USA persuading India?'. Newspaper media plays a crucial role in developing a personal view on any topic as people trust more on newspaper media than any other means of media. News headlines are articulated in such a way that it stands for the whole news. By doing the sentiment analysis on news headlines related to India in the USA newspaper can help Indian government to understand the USA sentiments in real time. In this research project Semantic Oriented Approach which is based on SentiWordNet lexicon and machine learning techniques such as Random Forest, Support Vector Machine, Nave Bayes, Long Short-Term Memory, Concurrent Neural Network used for sentiment analysis. Results and findings of these techniques can help Indian government to do real time sentiment analysis on news headlines related to India in the USA newspapers.

Item Type: Thesis (Masters)
Subjects: N Fine Arts > NE Print media
Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 06 Nov 2018 12:00
Last Modified: 06 Nov 2018 12:00
URI: http://trap.ncirl.ie/id/eprint/3444

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