Cross-Domain Shopping and Stock Trend Analysis
- URL: http://arxiv.org/abs/2212.14689v1
- Date: Fri, 23 Dec 2022 18:21:28 GMT
- Title: Cross-Domain Shopping and Stock Trend Analysis
- Authors: Aditya Pandey, Haseeba Fathiya, Nivedita Patel
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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