The Webscraper is a tool for incoming analysts to utilize while conducting research on financial topics, companies, or current industry news. The goal of this application is to be used as a tool to save time in the daily prep work of analysts, allowing a one way stop to pull all articles pertaining to a specific keyword at a certain point of time from chosen News sources. The output of URLs will be saved into a user-friendly file, titled with the date and inputted keyword. Our team includes Makenzie G, Ethan H, Slok J, Lara G, Rain T, and Stacey K.
- User-Friendly: Interface easy to use with possibility to change all aspects of search feature, from keyword to news sources.
- Secure Data Storage: All information inputted is securely saved on the user's device and is neither transmitted nor shared with any third parties.
- No Registration Required: No account is necesssary in the initial model.
- Download the attached Jupyter Notebooks and install relevant libraries
- Manage file saving location on local computer to keep track of prior searches.
This tool requires careful consideration on the side of the user. Users must enter specific keywords that are likely to yield clear and relevant results. Please avoid using single-word keywords or commonly used articles and conjunctions. Although there are filters in place to exclude the most commonly used phrases, there is no guarantee that all relevant websites will be identified from the initial keyword entry in the tool.
- Improved Organization & Data Storage: Introduce cloud-based storage options in addition to local storage, allowing users to access their search results from different devices securely. Implementing automated data backups can also ensure data integrity and loss prevention. Or, a potential app where immediate and results from your history are stored in an organized manner for the user to refer to, from the convenience of a mobile device.
- Keyword Screening: Develop a more sophisticated keyword optimization algorithm that suggests alternative keywords or phrases based on initial user input. This could include semantic analysis to understand context and relevance, further refining search outcomes.
- Use of Python's Pandas: Incorporate additional data analytics features using Python's Pandas, such as automated trend analysis and visualization tools. This would enable users to quickly identify key patterns and insights from the collected data, enhancing their decision-making process.
- Security for Users' Searches Although the initial model does not require users to log in, we hope to implement a user profile and password for logging in. This is to include security measures, as we aim to keep this information and search history private for confidentiality in analysts' work.