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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

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