Cross-Domain Shopping and Stock Trend Analysis

Aditya Pandey, Haseeba Fathiya and Nivedita Patel

Papers from arXiv.org

Abstract: This paper presents a cross-domain trend analysis that aims to identify and analyze the relationships between stock prices, stock news on Twitter, and users' behaviors on e-commerce websites. The analysis is based on three datasets: a US stock dataset, a stock tweets dataset, and an e-commerce behavior dataset. The analysis is performed using Hadoop, Hive, and Tableau, allowing for efficient and scalable processing and visualizing large datasets. The analysis includes trend analysis of Twitter sentiment (positive and negative tweets) and correlation analysis, including the correlation between tweet sentiment and stocks, the correlation between stock trends and shopping behavior, and the understanding of data based on different slices of time. By comparing different features from the datasets over time, we hope to gain insight into the factors that drive user behavior as well as the market in different categories. The results of this analysis can provide valuable insights for businesses and investors to inform decision-making. We believe that our analysis can serve as a valuable starting point for further research and investigation into these topics.

Date: 2022-12
New Economics Papers: this item is included in nep-fmk
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2212.14689 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2212.14689

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators (help@arxiv.org).