EPL Analysis: Sentiment and Predictive Analysis: Technical Report

Bashar, Fasial (2017) EPL Analysis: Sentiment and Predictive Analysis: Technical Report. Undergraduate thesis, Dublin, National College of Ireland.

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Abstract

Sentiment analysis is also known as opinion mining, is a machine learning method to extract sentiment from text and databases. Sentiment analysis is fast growing method used by many companies in many sectors of business to help them understand voice of people based on their online reviews or comments on social media like Facebook or Twitter. Sentiment analysis focuses to determine the attitudes, emotions and opinion of a person based on their text or document. Sentimental analysis algorithms can group the text or tweets based on the opinion, attitude and emotions.

Twitter is one of the major social media services, where people share their voice or opinions about everything. Football is one of the most popular topics on twitter where people share their opinion, from a tweet being about their favourite team or a rival team. Everyone has an opinion, which they like to share with the world, they can use twitter handle (@ManUtd) to get the message across the team or person they are talking about which people can add on their tweets.

The topic this project will focus on is Football and more specifically the English Premier League. Live Tweets will be gathered for matches involving multiple teams. Then the data will be analysed and machine learning algorithm will be used to score “Positive” or “Negative” for each tweet. All the score will be displayed through the help of visualization.

The main objective of the project is to find out how fans react during the premier league matches by collecting the tweets during the matches and running sentimental analyses on the tweets.

Item Type:	Thesis (Undergraduate)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites > Online social networks T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites > Online social networks
Divisions:	School of Computing > Bachelor of Science (Honours) in Computing
Depositing User:	Caoimhe Ní Mhaicín
Date Deposited:	27 Oct 2017 11:42
Last Modified:	27 Oct 2017 11:42
URI:	https://norma.ncirl.ie/id/eprint/2671

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