Paper Info
Title
Stock market analysis from Twitter and news based on streaming big data infrastructure
Abstract
Due to the rapid development of the web, services of social media and Internet of Things (IoT) are producing a huge volume of data in every second. This data is not only large, but also grows quickly and is difficult to analyze. Most of traditional big data framework can't process such data in real-time. For processing the data in real-time, many companies and researchers have started to develop new big data frameworks. The Apache Spark, Apache Flink and Apache Storm have been introduced for real-time data processing. With the new processing frameworks, it has become more efficient to analyze the streaming data. Stock market analysis is a hot issued domain to analyze the big streaming data. In this paper, we build a real-time processing system to analyze tweets for finding correlation with the stock market. System configuration, performance of our system is explained. With 77% accuracy of Twitter data classification, we got 80% of separation of increase/decrease of stock value.
Year
DOI
Venue
2017
10.1109/ICAwST.2017.8256469
2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)
Keywords
Field
DocType
Big Data,Streaming Data,Twitter Analysis,Text Classification,PCA
Data science,Data processing,Spark (mathematics),Social media,Computer science,Internet of Things,Stock market analysis,Data classification,Big data,Stock market
Conference
ISSN
ISBN
Citations 
2325-5986
978-1-5386-2966-6
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Chungho Lee100.34
Incheon Paik224138.80