Analyzing stock market movements based on sentiment analysis
Utilizing NLP for sentiment analysis of stock comments, providing scores to determine whether the comments are positive or negative. Analyzing the sentiment of comments enables the assessment of whether the stock is viewed favorably (bullish) or unfavorably (bearish). By establishing a predictive relationship between stock trends and sentiment expressed in comments, it provides insights for stock trading decisions.
The comment data is stored in TiDB Serverless, and the system utilizes JDBC for database connectivity. The crud function and statistics are based on TiDB Serverless. In this hackathon, we are amazed by the high performance of the HTAP, which is based on the MMP architecture.
Meerkat Movers
Our team is Meerkat Movers. There are two developer in the team, one is me, named Jacky, and the other one is Jim. We are from Shenzhen, China. Our team’s slogan is “Happy programming”. We enjoyed this hackathon.
The following is the project introduction
Application demo:
Demo address：
Github url:
Frontend: https://github.com/opkcloud/meerkat-tidb-app-h5
Python: https://github.com/yuan2006/sentimentanalysisweb
Java: https://github.com/opkcloud/meerkat-tidb-app-source
TiDB Cloud is a powerful and user-friendly cloud-based database platform.
It provides almost all SQL functions directly online, and integrates traditional OLTP and OLAP into one, that is, HTAP, which can realize high-performance data transaction services and real-time data analysis services.TiDB is a type of MPP database.TiDB performs better than traditional database in HTAP.
The guides and the HTAP test results are in the following website:
https://github.com/yuan2006/meerkat-tidb-app-source/blob/main/HTAP_en.md
To train a more intelligent model by a lot of comments from media platform to help the investors to make an investment decision.
The development plan for the remaining part of the hackathon:
Thank you