Show simple item record

dc.contributor.authorLwanga, Victor Kwome
dc.date.accessioned2018-10-23T10:16:07Z
dc.date.available2018-10-23T10:16:07Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/11071/5996
dc.descriptionThesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore Universityen_US
dc.description.abstractStock market price prediction has become an area of research and interest for several years now due to the many challenges in making accurate price predictions due to the volatility of the data. However, the stock market is not easily predicted. Movement in the stock market is influenced by various factors such as personal fortunes, political events, individual tastes, preferences and natural disasters. People can express all these through their sentiments and opinions on the social media platforms, financial news, and blogs. The stock price does not only rely on the law of demand and supply. People’s opinions and moods also have a substantial impact on the movement of the stock prices of a company. Recently, efforts to increase the accuracy of stock market predictions by including data from social media such as Facebook and Twitter has received a lot of attention. Social media can be regarded as an indicator of sentiments, and these are known to influence the stock market. Current models lack a clear interpretation, and it is also difficult to determine, which data is relevant for stock market prediction since there is an abundance of the same on social media. This study proposed the use of machine learning algorithms that will be utilized in Natural Language Processing (NLP) to get opinions and sentiments from social media on a particular company's stock to predict the stock market prices. Previous studies show that public mood, opinion, and stock market price have some relation to an extent. The research used Support Vector Machine with bigram feature to perform sentiment analysis which exhibited and accuracy of 83 percent and Artificial Neural Network in Stock price prediction which had a mean squared error of 5.6. This research has proven that sentiment analysis can be incorporated in stock price prediction.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectStock marketen_US
dc.subjectData volatilityen_US
dc.subjectSupport Vector Machineen_US
dc.subjectNatural Language Processingen_US
dc.titleStock market price prediction using sentiment analysis: a case study of Nairobi stock exchange marketen_US
dc.typeThesisen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record