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How did you select the subreddits? Most seem to match. I found that The Last Airbender: The Legend Of Korra has /r/Korra with 1.7k subscribers, but there's also

How did you select the subreddits? Most seem to match. I found that The Last Airbender: The Legend Of Korra has /r/Korra with 1.7k subscribers, but there's also /r/legendofkorra with 200k subscribers and more rewatch threads. > My regexes grew gnarly from occasional tweaks, so I ripped it all out and changed up my process a bit. Are you still using regex? There was a thread recently about collecting book recommendations from HN comments [1]. The app creator used deep learning and fine-tuned a BERT model with their own data. The results were impressive and I think it could work with unstructured data like yours too. I don't know what's your stack, but anything with Named Entity Recognition (NER) would be a good start. I really like the lists you compiled. Bookmarked. 1 - https://news.ycombinator.com/item?id=28595967 https://news.ycombinator.com/item?id=28595967

hoten 5y ago
Most of the subreddit/show pairs came from here: http://tv-subreddits.wikidot.com/ http://tv-subreddits.wikidot.com/ . Sourcing this data is the tricky part: I can't find any other sources. Idea I just had: maybe searching /r/television posts for "/r/..." links would be a good lead? > Are you still using regex? Yup, just in a much better way. I won't bother explaining how it was before, but now: I have a collection of regexes that try to match a "season" and "episode" at the same time. If that fails, I fall back to matching for them separately. If "episode" is not found, I ignore the thread as a possibly related to a specific episode. If I have further issues with the parsing, I'll consider a whole new approach like you've recommended. But I don't have any experience with that stuff so my first thought is that it'd be overkill.