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Irish Business Predictions by Twitter Data Mining: Technical Report

Villalba, Lucas (2022) Irish Business Predictions by Twitter Data Mining: Technical Report. Undergraduate thesis, Dublin, National College of Ireland.

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The key point of this report is to find trends in social media communication(Tweets from Twitter) and company stock prices during the same period by analysing both social media and stock prices. Tweets drive stock, or the stock moves tweets.

There are indicators from the company performance side, such as revenue growth, revenue per client, profit margin, client retention rate, and customer satisfaction. Otherwise, this report will implement statistical government data from the Annual Business Survey of Economic Impact 2019 and private data as the current stock price of each company. Due to data exploration, The ABSEI 2019 is available from The Department of Business, Enterprise and innovation, part of the Irish government website offering access free to this novelty data resource in Excel readable format. With information divided by sectors and Irish owned companies with figures in euros from 2000 to 2019.

The private data, such as current stock price data, is also free from the Yahoo Finances website. Using the RStudio to extract and process this data comes in time-series form. The stock price by time with daily values such as open price, high price, low price, close price volume, and adjust—many potential uses for time-series.

On the Social media side, Twitter will be the target data source. The access and processing of data by RStudio, called Twitter Data Mining, will study patterns and trends concerning company performance from the social communication side. Natural Language Processing, which combines linguistics, computer science, and artificial intelligence, will be used as part of this process.

Contrasting and crossing the data above will tell us how sound companies perform on the stock market and how they drive their communication.

“...language is never innocent.”
― Roland Barthes.

Item Type: Thesis (Undergraduate)
Subjects: 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
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
H Social Sciences > HG Finance > Investment > Stock Exchange
Divisions: School of Computing > Bachelor of Science (Honours) in Computing
Depositing User: Clara Chan
Date Deposited: 15 Sep 2022 08:53
Last Modified: 15 Sep 2022 08:53

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