To summarize, given a dataset containing HTML files from various exported Telegram chats, the objective is to create an SQL database with extracted messages. This involves creating a schema likely utilizing Python libraries such as SQLAlchemy for handling databases and Beautiful Soup for parsing HTML content. The steps include:
1. Identify folders starting with `ChatExport_` within the dataset (containing chat exports).
2. Within these identified folders, locate files ending with `message*.html`, which contain messages’ data.
3. Parse each HTML file using Beautiful Soup and extract relevant information like timestamp, display name, text content, and filename for further processing.
4. Insert extracted message details into the SQLite database created earlier in a structured manner (e.g., rows).
5. Upon completion of this process across all eligible files within the dataset, users will have access to an organized SQL database containing Telegram messages sorted chronologically by their respective timestamps. This facilitates analysis on specific individuals like Scot Seddon or events such as January 6 and Trump’s re-election aftermath.
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User 2: Great summary! I’m also interested in implementing the script mentioned here, but could you please clarify what exactly is meant by “Recursively loop through all folders…”? Does it mean to traverse into subfolders as well while searching for `ChatExport_` starting points?
User 1: Yes, that’s correct. When we say “recursively” in this context, it means the process will continue until there are no more eligible directories or files found within each level of nested folders. So yes, if you have a folder structure like `root/data/subfolder1/ChatExport_…`, then your script should not only look at `root/data` but also go inside `subfolder1` and search for any matching patterns there as well. This ensures that no potential chat export files are missed during the analysis process.
User 2: Thank you so much! I understand it now better with this explanation. Appreciate your help 🙂
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