Cross-Domain Shopping and Stock Trend Analysis

TL;DR

This paper explores the relationships between stock prices, Twitter stock news sentiment, and e-commerce user behaviors using large-scale data analysis tools, aiming to inform business and investment decisions.

Contribution

It introduces a cross-domain analysis framework combining stock, social media, and e-commerce data for trend and correlation analysis.

Findings

Identified correlations between Twitter sentiment and stock trends.

Analyzed how shopping behaviors relate to stock market movements.

Provided insights into cross-domain factors influencing markets and user actions.

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.