CashTagNN: Using sentiment of tweets with CashTags to predict stock market prices

Neeraj Rajesh, Lisa Gandy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

In this paper we discuss a system, CashTagNN, which uses the sentiment and subjectivity scores of tweets that include cashtags of two companies, Apple and Johnson and Johnson, to model stock market movement, and in particular predict opening and closing stock market prices. We demonstrate that by using only sentiment and subjectivity along with a neural network machine learning model we can predict the opening and closing prices of the two companies with high accuracy.

Original languageEnglish
Title of host publicationSITA 2016 - 11th International Conference on Intelligent Systems
Subtitle of host publicationTheories and Applications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509057818
DOIs
StatePublished - Dec 5 2016
Event11th International Conference on Intelligent Systems: Theories and Applications, SITA 2016 - Mohammedia, Morocco
Duration: Oct 19 2016Oct 20 2016

Publication series

NameSITA 2016 - 11th International Conference on Intelligent Systems: Theories and Applications

Conference

Conference11th International Conference on Intelligent Systems: Theories and Applications, SITA 2016
Country/TerritoryMorocco
CityMohammedia
Period10/19/1610/20/16

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