Predicting Automotive Brands Popularity Using Twitter Data

Efendi, Stevent and Erwin, Alva and Eng, Kho I (2015) Predicting Automotive Brands Popularity Using Twitter Data. Bachelor thesis, Swiss German University.

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Abstract

Begin typing the abstract here, 1.5 spaced. The abstract must include the following components: purpose of the research, methodology, findings, and conclusion. The body of the abstract is limited to maximum 200 words. The abstract may consist of one or more pages. The first page is formatted the same as a first chapter page; subsequent pages are formatted the same as general text pages. Begin typing the abstract here, 1.5 spaced. The abstract must include the following components: purpose of the research, methodology, findings, and conclusion. The body of the abstract is limited to maximum 200 words. The abstract may consist of one or more pages. The first page is formatted the same as a first chapter page; subsequent pages are formatted the same as general text pages. Begin typing the abstract here, 1.5 spaced. The abstract must include the following components: purpose of the research, methodology, findings, and conclusion. The body of the abstract is limited to maximum 200 words. The abstract may consist of one or more pages. The abstract must include the following components: purpose of the research, methodology, findings, and conclusion. This is to give the example of abstract with the length of exactly 200 words.

Item Type: Thesis (Bachelor)
Subjects: H Social Sciences > HF Commerce > HF5823 Branding (Marketing)
H Social Sciences > HM Sociology > HM742 Online social networks
H Social Sciences > HM Sociology > HM742 Online social networks > HM742.1 Social Media
Q Science > QA Mathematics > QA76 Computer software > > QA76.91 Data mining
T Technology > T Technology (General) > T58.5 Information technology
Divisions: Faculty of Engineering and Information Technology > Department of Information Technology
Depositing User: Atroridho Rizky
Date Deposited: 05 Nov 2020 04:33
Last Modified: 05 Nov 2020 04:33
URI: http://repository.sgu.ac.id/id/eprint/1727

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