Sentiment Analysis with BERT Neural Network and Python
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- čas přidán 12. 07. 2024
- Want to leverage advanced NLP to calculate sentiment?
Can't be bothered building a model from scratch?
Transformers allows you to easily leverage a pre-trained BERT neural network to do exactly that!
In this video we'll go through how to get up and running with Hugging Face Transformers and BERT to be able to calculate sentiment. We'll run the model using a single prompt but also leverage BeautifulSoup to scrape reviews from Yelp to be able to calculate sentiment on a larger scale.
In this video you'll learn how to:
1. Install Transformers
2. Perform Sentiment Scoring using BERT and Python
3. Scrape reviews from Yelp and Calculate their Sentiment
Get the code for this tutorial: github.com/nicknochnack/BERTS...
Thanks so much to Mito for sponsoring this video! Mito is an amazing JupyterLab plugin that allows you to work with your data easily using a drag and drop interface that's similar to Excel. Try it out for FREE at trymito.io! If you need a little help getting started, check out the tutorial: docs.trymito.io/getting-start...
Chapters
0:00 - Start
0:23 - How it Works
1:48 - Tutorial Start
2:55 - Install and Import Dependencies
9:20 - Setup BERT and Transformers Neural Network
11:27 - Encode Sentences for Modelling and Calculate Sentiment
16:40 - Scrape Reviews from Yelp using BeautifulSoup
22:41 - Load Reviews into Pandas and Calculate Sentiment
29:52 - Run the Sentiment Pipeline for Multiple Reviews
31:32 - Wrap Up
Oh, and don't forget to connect with me!
LinkedIn: bit.ly/324Epgo
Facebook: bit.ly/3mB1sZD
GitHub: bit.ly/3mDJllD
Patreon: bit.ly/2OCn3UW
Join the Discussion on Discord: bit.ly/3dQiZsV
Happy coding!
Nick
P.s. Let me know how you go and drop a comment if you need a hand! - Věda a technologie
I love how you take your time to explain each sentences and what they do! Absolutely love it!
Very helpful as a beginner!
Keep up
BERT with multilingual benefit is great advantage for analysis of text (especially with large-diverse dataset ) . This is enlightening ! Thank you Nik
Thanks again for this tutorial Nick. My challenge is to 100% finish all your tutorials. 3hrs, 5hrs, 8hrs, 100hrs. all of it.
amazing video! I mixed the content from your code with a csv file that I got from extracting tweets and now I can do a sentiment analysis on so many subjects! Thank you!!
hi im trying that as well, did you convert your file to a data frame like in the video?
These kind of content is what most budding ML enthusiasts need. Thanks for this
Anytime, so glad you enjoyed it @Sahil!
@@NicholasRenotte Hey, pls do a detailed video on Beautiful Soup. Also my request is pls make a video on other NLP usecases - text summarization, topic modeling, NER, text generation, Q&A with pretrained models (BERT, ROBERTA, GPT and so on)
Your channel is a treasure for Data Scientist.
Amazing video! Simple, structured, keep up the good work !!! P.S This helped me so much on my course lol
You made it sound so simple! This is apt for beginners like me. Thank you ^^
I love the way you teach. I'm going to do my final year project on sentimental recommendation system. This will help me a lot
Excellent video explaining multiple concepts. Thank you for the great content!
Incredible work, love your explanations. Thank you!
brother I wanna say that your videos are very easy to understand and along the way to follow practically in our computer please make complete course on NLP and OPENCV And Machine learning because you are very good teacher I very like you god give you long life with a lot of happiness
You are a life saver man. i was stusked with my research. you saved me.. i understood the basics. Thanks
Awesome video. Simple and easy to follow. Thank you.
Awesome video, so neatly explained step by step --- thanks a lot
Very good pal. You keep it simple and at high level.
I would be happy to see those two negative votes to elaborate further why they are not satisfied though..
Love this video! You have an amazing skill in teaching others and make it look so simple.
I'm subscribing you and I guess on my journey learning python I will watch a lot of your videos. Thanks a lot!
Hey Nik!
I've been looking left and right for a guide on how to interpret the logits from BERT and thank god you've uploaded something that explains it well! I liked and subscribed.
(Maybe you could refer me to a source, where you happen to learn it? - looking one for the FinBert variant)
Also, your crash course on that scraping part is straight on point and I love that you executed each line from it to show you how it gets transformed.
You are truly blessed with an out-of-this-world tutoring skill!
Thanks sooo much @Capt_Kaplan! I'm kinda just doing a lot of googling to learn it atm, there's heaps of examples of custom models in the HF model repo though, check this out: huggingface.co/ProsusAI/finbert
An excellent tutorial. Thank you much for this.
Great video. Would love to see a scraping tutorial. Especially one where you could pass credentials and log in first
Awesome stuff man, helping me on my project
Great video! simple and quick to get good results thanks a lot!
This is best content on bert .. loved the explanation ..
I'm a new subscriber.., loving ur videos already... Cheers man all the best
Thanks so much @First Attempt, welcome to the fam!
Great content, great delivery .... overall great video
Awesome video, now I need to find a Brazilian Portuguese trained model asap to test it.
Thanks!
Good conceptual video. I was your 1k'th like
Great vid! Not one of the more popular requests here but would be interested on a deep dive in building scrapers. It's kind of a niche topic since it's not the most exciting part of ML or Data Science but at least I get to throw my hat into the table. Haha. Looking forward to more vids =)
You got it @John, was planning on doing a live stream on it! But might put it into my Python for Data Science course that's coming!
Great job, Nicholas!
Sweet video, loved it!
Awesome video! If its to the extend of your knowledge, a subsequent video about fine-tuning a pre-trained model to a particular dataset would be very interesting. So train, test and validate the accuracy of a model pre- and post-training. I have read some articles which showed accuracy of a model can be improved significantly when trained on the particular dataset.
Definitely, plus the models become a lot more practical when tuned to your use case. Definitely will have a series of tutorials or one mega tutorial coming on it soon @Michael!
@@NicholasRenotte did you make a tutorial about this issue already ?
I couldn't like your video more,, it's simple, informative, short and just straight to the point :)
Thank you
Love your videos bro 😉
Feel like lot to learn
Thanks a bunch @Dheeraj! Definitely always something new!
exactly what i was looking for thank you you made my day🥰🥰🥰
Amazing video!
Yes, i think a bigger web scraping tutorial would be beneficial
Did he make a tutorial about it?
Hello Nicholas Reynolds, I am a college student, I like your video very much, I keep learning, you are my half teacher, thank you very much, look forward to more videos
Thanks so much! Stick around, plenty more mega courses to come!!
really loved your video. Would like to see if you can do same video but for stocks such as NVDA or TSLA. Thanks.👍
Very good explanation and content
Thanks so much @Muzammel! So glad you enjoyed it.
Great tutorial thanks
Thank you for making a video on this
Anytime! So glad you liked it!
Appreciate your work sir
Thanks for checking it out man!!
Muy util. muchas gracias por la informacion. Saludos desde Mexico
Thank you so much !
Again best explanation
Ayyye, thanks so much @Ameer!
Cool and informative.
awesome explanation. however i have some questions, is text preprocessing included in the BERT model? and what is the thing with 'x[:512]' limitation?
Amazing!
Great Thank you.
Nice vid.
It's an application of BERT, does not quite add much new science... But good enough for some hands on with it.
Keep it up 👍
Yah, kept this one super high level. Can definitely get way further down into the weeds!
Great work bro..pls make a video on activity recognition using mediapipe
You got it!
Thank you
i like so much your cours
Awesome as always!! I really appreciated this. Did you finally made a web scraping for data science super tutorial!??. Please tell me you did!😃
Awsome buddy
Thanks a bunch @Shanthi!
this is cool!
Thanks so much!
Fantastic explanation on all videos. just wondering whether you can do a video on sktime - installation, introduction, processes , etc. Hope to hear from you. cheerio
Haven't heard of it before, is that a timeseries lib for scikitlearn? Sounds awesome if so!
Would love a video on fake news detection.
Thanks for teaching us!
Ooooh, yeah that'd be sick, I'll add it to the list!
@@NicholasRenotte Thank you
Have a nice day
@@heisenbergwhite5845 you too!
Another great tutorial thanks man
Have one question in this video we are able to extract reviews from page 1 only so how to extract reviews from all the pages?
Thanks once agian
Could loop through the subsequent pages as well @Nandan! Would need to build a more sophisticated scraper for that however.
Hey Nicholas, is there any way of changing the bert sentiment score from 1-5 to a floating value between 0-1, similar to for example the vader sentiment score? Or do I need another bert sentiment model for this? Thanks in advance :)
Need another BERT model @Michael, this one is specifically trained to give you a binary value.
Excellent Video.
Can you point me to the changes to be made if TensorFlow is used?
wonderful
Awesome video! But, I was wondering if text preprocessing (removing punctuations, stopwords, lemmatization, etc) is needed before encoding the reviews? Appreciated if can get an answer
Yes, in the preprocessing we remove all the noise from the data.
You’re the best around!! Thank you for this!!
One question: is it possible to extract comments from a Facebook post?
Thank you.
I think it's pretty tricky, FB is notoriously hard to scrape. Can probably get your own comments via the API?
Hello Nicholas, many thanks for the vidoe, how can one implement this code in tensorflow or what advantage pytorch has over tensorflow in Bert implementation?
Thank you for the video. Can I run a content analysis on short Customer Feedbacks that's releable?
Hi Nicholas, thank you for the video! It's really helpful for my work. Also, I have a question that is it a must to do text pre-processing by applying NLP model using deep learning? because it seems like you didn't apply any text preprocessing method for the raw text you've scraped.
For these models they're able to handle raw unprocessed input as they have a pre-processing pipeline built in @lowwwleechi. If i got sucky results I might add some additional pre-proc to improve the results!
@@NicholasRenotte Got it! Thanks a lot!
Very good !!!! IBM
instead of using a lambda function for adding the review scores to the dataframe, would it be equally as good to use list comprehension to get a list of the scores then creating a new column out of that in the dataframe?
Hmm this is helpful for reviews but not for pos/neu/neg sentiment analysis, which is what I was actually trying to get some examples of regarding score interpretation.
scraping a web page and using that data as training data within a chat bot would be very interesting.
Hello Nicholas,
Is it recommend to code all the machine learning algorithms from scratch so that I can learn math behind it or just understand and start to code?
I don't personally think so, I think it's more important to know how to use them as a Data Scientist. If you're looking to get into research however you would probably need a deeper understanding however!
Hey Nicholas Great Video!!! I was wondering if I could change the url in the code and give the class name of another website's comments other than YELP will it still work...
hey what a great videos, can you make a tutorial for sentiment analysis deployment to a simple web?
Hi Nichola, thank you for this video. Good information 👍 I have a doubt, can it handle sarcastic reviews? Also don't we remove shop words from text data?
Ooooh, I doubt it tbh. You can definitely do stop word removal, I was just being a little lazy in this.
Hi, I'm just learning NLP and it's totally new to me. Just want to know how to know how tokens / text can be passed for particular nlp pipeline? Thanks in advance
Hi Nicholas , can you explain me how can I scrape rating of particular user from the webpage you mentioned
Thank you so much. Your video is so helpful. Is it possible to make another one about how to do sentiment analysis with BERTweet for covid -19 tweets? Thanks again.
You got it! Working on a bunch of NLP stuff atm.
@@NicholasRenotte Did you make a video about BERTWEET? I didn't find it.
@@ey5722 nope, not yet.
Please make a video tutorial on Web scraping as well.
Can you make one on mT5 or m2m100 for language translation models and how they can be trained from huggingface. Would be super helpful for techies across countries where content is limited.
You got it! Got a ton more HF stuff coming as well.
May I know if this support a multi-language in one sentences or in one review?
Do you currently have a video that covers how to do this? ->> "Can't be bothered building a model from scratch?". i would like to learn how to build or fine tune BERT. thanks
What’s the logic behind setting the slice up util 512 when we were making our predictions?
Great video, Is there a package for Spanish or only in English?
Multi-langual and Dutch too... A double Heineken for u🍻
Maybe another vid of text classification for different classes/categories based on Bert ( Bert-base, multilangual cased) Not specific sentiment but categorizing sentences/text into subjects like cars, beers,soccer...
You got it, actually started work on a mega NLP tutorial. Zero-Shot Classification would be perfect for that!! Can't wait to drink that Heineken this weekend, still hoping Hamilton beats Verstappen though 😂.
@@NicholasRenotte Looking forward👍
Until this new season the F1 races were dominated by Mercedes. It is like running tensorflow with gpu versus tpu 😁 If Verstappen or other talented drivers got the same racing car as Hamilton then the competition would be more interesting .... But of course I hope Verstappen will win!
@@henkhbit5748 you almost had your wish this weekend 😂 if those tyres didn't blow Verstappen was on a dream run!
@@NicholasRenotte Yes the anomaly detetection algroritjm of Red Bull could be improved and I think Hamilton has not trained with the rigt AI Gym RL algorithm 😎
@@henkhbit5748 😂 😂 😂
Is there a bert multingual up to date? This program you are using, uses old versions of numpy... between others. If there is an up to date version, it would be amazing to know, where to find it, thanks!
if not, could you especify the version of nunmpy that was used
Thank you for sub Thai💖🥺🙏
How would I fine tune this because right now im getting some inaccurate reviews. For some some reviews it is giving me 3 when it should be 4 stars or it is giving 5
Can we add fully connected layer on top of that model to use it for other text preprocessing tasks 🤔
Could probably pass through the logits to a subsequent layer!
Hello Nicholas!, i got a question is there any ML that can extract keywords from a text?
Could use Named Entity Recognition to extract keyword types. If you're just looking for specific keywords you could just use a find function!
@@NicholasRenotte Thank you, love your work!
I have a problem: RuntimeError: Failed to import transformers.models.bert.modeling_bert because of the following error (look up to see its traceback):
No module named 'torch.utils'
bert is too big (1GB for the one with good quality). What would be the best way to deploy it with my django back end? Any recommendations?
Deploy it as an API or use a pre-hosted instance (they're available direct through HF Hosted)
@Nicholas Renotte can u help me or tell me how to apply word embeddings in BERT for text classification
can you explain the advantage and disadvantage of using pre-trained model in our data?
Can BERT be used on time series data I wondered ?
It's mainly for NLP :), take a look at the Facebook prophet vids for time series forecasting!
How to validate the output of the sentiment?
you should be downloading videos every single day.
Hello, instead of doing the first 512 tokens, aren't you just taking the first 512 characters? Lov your videos btw :)
Love mito but had them on a zoom call - it does not work with streamlit app. Perhaps it does not work with any app outside of a Jupyter notebook not sure 🤷♂️
Can you do sentiment analysis using apache spark - pyspark mllib
Hey! When I change the website to another one (like Amazon), it doesn't go with it. Please help me with it