Hey Gagan, thanks for this. However, I am facing a data dimension mismatch and the expected input size mismatch. Stated below RuntimeError: The expanded size of the tensor (615) must match the existing size (514) at non-singleton dimension 1. Target sizes: [1, 615]. Tensor sizes: [1, 514]
hi gagandeep kundi, can you show list of users that submitted ratings only? I already follow this step, but only showing the reviews and rating. but cannot for user submitted the ratings only
hello Gagandeep, if i'm using jupyter notebook on browser, is there any way to not restart the whole notebook? there is a part where we import pipeline which takes a lot of time, considering it is 1.43 GB or more. thanks in advance!
Hi Gagan, thanks a lot for this.. have been looking for this for a while By any chance, were you able to shoot the data engineering part ? Like how to get this data in a database ?
Hi. Thanks for this. But wanted to know how to scrape all the reviews ( you have scraped only 5 reviews. Most of the reviews will appear once we click "See all reviews" in a pop up). Any method to scrape all the reviews?
Hi Vinay, all the reviews that could be scraped were scraped. Using pandas dataframe head function you get to see only 5 rows, you can see more by adding an argument like df.head(20) and now you'll be able to view 20 reviews. I'd recommend watching some basic Pandas tutorial to get the most out of this video.
I have gone through thousands of videos in youtube this is the only video which explians the google play scraper properly . Thanks Brother
Thank you so much for this video! Great explanation and very easy to follow even as a beginner in python programming.
Thanks a lot. This was so helpful and it saved me a lot of time.
Hey Gagan ! Thanks for this. You made it so simple ! Learned a lot
Hey Gagan, thanks for this. However, I am facing a data dimension mismatch and the expected input size mismatch. Stated below
RuntimeError: The expanded size of the tensor (615) must match the existing size (514) at non-singleton dimension 1. Target sizes: [1, 615]. Tensor sizes: [1, 514]
Mr. Gagan. Thanks for sharing this video. With this, only 200 reviews are scrapped. What needs to be done if we need to scrap all the reviews?
great vid brother 👍
hi gagandeep kundi, can you show list of users that submitted ratings only?
I already follow this step, but only showing the reviews and rating. but cannot for user submitted the ratings only
Great explaination sir!!
Thank you for this tutorial!
thanks for this, it is help me to scrapping other apps
hello Gagandeep, if i'm using jupyter notebook on browser, is there any way to not restart the whole notebook? there is a part where we import pipeline which takes a lot of time, considering it is 1.43 GB or more. thanks in advance!
What about the replies from the developer? Is there a way to scrape it as well?
Bro Hinglish reviews ka kaise sentiment analysis karey, Google translate ki api work ni Karri
Hi Gagan, thanks a lot for this.. have been looking for this for a while
By any chance, were you able to shoot the data engineering part ? Like how to get this data in a database ?
Sure, I'll see if I can find some time this week.
how to deploy this model into web plz share that also
Great one bro
cannot routh to file path
Hi. Thanks for this. But wanted to know how to scrape all the reviews ( you have scraped only 5 reviews. Most of the reviews will appear once we click "See all reviews" in a pop up). Any method to scrape all the reviews?
Hi Vinay, all the reviews that could be scraped were scraped. Using pandas dataframe head function you get to see only 5 rows, you can see more by adding an argument like df.head(20) and now you'll be able to view 20 reviews. I'd recommend watching some basic Pandas tutorial to get the most out of this video.
Thank you 🙏🏽
please send the code
How long could this take to analyse about 50000 rows of reviews
Hi Michael, time can vary depending on the compute resources. For the fastest performance, try Google Colab with GPU.
Where is the source code please?
github.com/kundigagandeep/sentiment_analysis
@@gagandeepkundi8855 Thank you so much.
Good content but awful screen resolution and presentation! Do not ruin your effort by producing low quality material