Network of The Witcher | Relationship Extraction & Network Analysis with Spacy & NetworkX

Can this be used for scientific papers? I have tried manual, line-by-line coding, and then visualizing the overlapping themes across papers, but this doesn't seem to be trustworthy...

great video. thank you!

Thank you very much for these kinds of videos. I am currently working on 2 projects that require these two techniques, and this video has been very helpful to me. Thank you so much!!

We NEED more of this kind of projects!! I love this series so much... So please, we need the finale of this project...

Thank you very much Madam.

next episode? I can't find it :´v

this video was sooo helpful!!! thank you.

Is the next video on graph analytics out?

Hi, your video was awesome, but i have question to the pyvis graphics. I have the problem that the nodes moving extremely fast and i cant fix that😢 can you help me? I use exactly the same code only another dataset.

This was great, when do you plan to do the 3D network graph visualization video though??

I really love your work! Good job there, very inspiring

Imagine doing this in german where all the nouns start with capital letters 😃

As always thank you, Thu Vu for the great content 🦾

Taking only first name of characters doesn't work:
sent_entity_df_filtered['character_entities'] = sent_entity_df_filtered['character_entities'].apply(lambda x: [item.split()[0] for item in x])
Any solutions?

Why was the whole NER (NLP) part necessary, if the character names were already identified via scraping? Wouldn't it suffice to just loop over the tokens of the book and select for each sentence the token if it is found in the list of characters?

Love your videos. Is it possible to make a video regarding to the data cleaning, such as how to treat outliers, etc? thanks

Hi! just wondering if we could do the same thing with gephi?

More awesomeness like this please, data visualisation is my favourite 😀

Thanks!

Thank you!! Thu Vu

Thank you thank you. I love learning by doing projects so this is perfect. Thank you! 😀

Excellent video. Thank you

I love this tutorial, but when I need to get my output, I'm getting only the following message:

"Local cdn resources have problems on chrome/safari when used in jupyter-notebook."

I have Jupyternotebook integrated with VS Code, I want the output as you get it in the video.

The only way I can get the output is by opening manually the saved html file in te browser.

Seriously, Data Science is just mind blowing! Specially when you consider all the real-world applications for those resources.

Thanks Thu Vu, really enjoy your informative videos! Would you be able to do a video on how you document and organise your data science projects as well? E.g. there was a snippet of how your created a separate python file in the utils folder to store your functions. It would be really helpful to see in greater detail how the whole project folder was organised. Thanks again!

I LOVE the copy pasting thing you do. No time wasted typing. Thank you! You're awesome.

Phenomenal!

My partner will love this! Thank you :)

This video is awesome! Great job Thu!!!

This was so entertaining to watch, never knew you could do that with nlp, thanks for this amazing video!!! i'll try latter with different fantasy books.
Hope you have an amazing week!!!

Really well explained code, it's rare to find such a good description of an algorithm. Thank you!

Very cool visualization and awesome topic!

Hey! Great video, thanks a lot! Awesome to see these libraries applied to an interesting problem in a cool way!
I would just have one question: what is the value of using NER and spacey in this case? If I understand correctly, you already had all the entities (characters) so perhaps the tokenisation of sentences so that you can calculate the rolling window? At the same time, wouldn't it be easier to make an assumption about the avg wordcount of 5 sentences, scan the document for your characters and their position, calculate their relative position and get the weights?
I'm curious if there is something I missed (feel free to point out if what I said doesn't make sense)
🙂

Love your content. It's easy to follow. Can't help but feel sad for the hair though

Please make a tutorial about association analysis!!

Amazing video and amazing channel ! Thanks a lot !

You are so smart

Great Method!, one question: Which cam model do you recorded this video? , Thanks!

Hey Thu Vu, this stuff is really very cool!! Watching what amazing things we can do using Data Science excites me. Thanks for sharing it with us.😀

Great video, enjoyed it very much. And great choice of subjects, turn what had the potential of being "a very boring lesson/tutorial" into an exciting adventure that had me clinging to my chair thinking "what are we going to discover next?"

Very cool !!

Why can you enter codes instantly?

amazing

Thu, I absolutely love this video series and lost count of how many times I have watched it for tips & tricks. Thank you 🙏 can’t wait for the computation.

Was completely captivating from start to finish

Hey @thuvudataanalytics you may want to re-check home of your play ▶ lists 📃 don't have accidental 3rd party entries, ie the DSci Playlist has some yoga 🧘🏻‍♂️ & other unrelated posts. FYI.
PS awesome tutorials!

Really proud of you. Cuz you are a vietnamese

Very interesting content. Thank you! 🙏🏻

Graph algorithms are quite useful as well as beautiful in their own right.
I’m glad there are some people using them.
More importantly, use them with a fun-to-do use case.

Thanks for your effort.