Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Stock Market Prediction using Social Media Analysis
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Stock Forecasting is commonly used in different forms everyday in order to predict stock prices. Sentiment Analysis (SA), Machine Learning (ML) and Data Mining (DM) are techniques that have recently become popular in analyzing public emotion in order to predict future stock prices.

The algorithms need data in big sets to detect patterns, and the data has been collected through a live stream for the tweet data, together with web scraping for the stock data. This study examined how three organization's stocks correlate with the public opinion of them on the social networking platform, Twitter.

Implementing various machine learning and classification models such as the Artificial Neural Network we successfully implemented a company-specific model capable of predicting stock price movement with 80% accuracy.

Place, publisher, year, edition, pages
2015.
Keyword [en]
Statistical Learning; Artificial Intelligence; Neural Network; Machine Learning; Support Vector Machine; Twitter; Stock Forecasting.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-166448OAI: oai:DiVA.org:kth-166448DiVA: diva2:811087
Supervisors
Examiners
Available from: 2015-05-12 Created: 2015-05-10 Last updated: 2015-05-12Bibliographically approved

Open Access in DiVA

fulltext(995 kB)4041 downloads
File information
File name FULLTEXT01.pdfFile size 995 kBChecksum SHA-512
6ee2d573be4547b5eb712536959e54c5a867b0473d0fa15696bc94f569a6db31e3b0a7eeeabd160405cd43fd602896df70279ff326c3677dd60b1a2ec0455300
Type fulltextMimetype application/pdf

By organisation
School of Computer Science and Communication (CSC)
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 4041 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 3893 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf