NORMA eResearch @NCI Library

Identifying the Impact of Tweets on Kickstarter Campaign’s Funding Using Sentiment Analysis and Machine Learning Approach

Bukane, Nilesh Ramesh (2019) Identifying the Impact of Tweets on Kickstarter Campaign’s Funding Using Sentiment Analysis and Machine Learning Approach. Masters thesis, Dublin, National College of Ireland.

[img]
Preview
PDF (Master of Science)
Download (1MB) | Preview

Abstract

Kickstarter is the popular crowdsourcing platform, which is used for pitching new ideas and products and campaigning for the same. The campaigners usually invest their significant amount of money, time and resources for pitching their products and for putting things in operations. If the information about the project’s successful funding’s is available to campaigners during the campaign, it will help them significantly to make the necessary arrangements and take crucial decisions about their products being campaigned. This will help them to save lot of their money and resources. Also, these predictions will allow potential funders to take decisions wisely related to their investments. The tweets on social media such as Twitter are the form of people’s opinions and thoughts and has the potential of making an impact. Therefore, in this research, the impact of sentiments within the tweets on project funding is been identified. For this analysis, the lexicon-based sentiment classification technique was implemented for classifying the tweets. After classification, it was identified that there is a significant association between the high funding’s of the project and the positive tweets, and between the low funding’s of the project and the negative tweets. Based on these findings, the machine learning models were trained using SVN, Random Forest, KNN, Naïve Bayes and Decision Tree algorithms. These trained models can be used to classify the textual information into sentiments and may further help in predicting whether the particular project has a tendency to be successfully funded.

Item Type: Thesis (Masters)
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
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 27 Nov 2019 12:29
Last Modified: 27 Nov 2019 12:29
URI: http://trap.ncirl.ie/id/eprint/4108

Actions (login required)

View Item View Item