Change search
ReferencesLink to record
Permanent link

Direct link
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
Keyword [en]
Statistical Learning; Artificial Intelligence; Neural Network; Machine Learning; Support Vector Machine; Twitter; Stock Forecasting.
National Category
Computer Science
URN: urn:nbn:se:kth:diva-166448OAI: diva2:811087
Available from: 2015-05-12 Created: 2015-05-10 Last updated: 2015-05-12Bibliographically approved

Open Access in DiVA

fulltext(995 kB)1074 downloads
File information
File name FULLTEXT01.pdfFile size 995 kBChecksum SHA-512
Type fulltextMimetype application/pdf

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

Search outside of DiVA

GoogleGoogle Scholar
Total: 1074 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

Total: 1762 hits
ReferencesLink to record
Permanent link

Direct link