Dear Viewers, a lot of time and effort goes into making these videos. So, all I ask of you is to subscribe to my channel (this would be at no extra cost to you!) and hit the like button if you find the video useful. This would really help my channel. Thanks in advance! - RCM.
Hi sir glad to visit your channel. Nice content and i tried your code and it worked as you have shown in the vedio but last some of the codes you are not used in the vedio whats the purpose of the code?? Can you explain me that pls.
@@TheAIandDSChannel You showed that codes first in the vedio in that you said for live ones try the same code and what is that u can't understand that one ❤️
@@jeevanasenthilkumar 0:29 shows how to work with live data from Twitter. In order to work with the live data, you need to have a Twitter developer account which gives access to Twitter API from where you can pull the live data. This is a bit time consuming process and hence was avoided in this video.
I want to ask you keyboard shortcut, the way you change the pos_tweets to neg_tweets by selecting only 'pos' word and replacing it with 'neg' word from 16:10 to 16:14 of the video.
Thx for the detailed demonstration! I do feel curious though if you already have a way to generate sentiment score why bother running machine learning on it?
You're Welcome!. We are generating sentiment scores to get labels for the text data. Sometimes this label is provided in the dataset. We then use this text data, and sentiment polarities to train the machine learning model so that we can use the machine learning model to predict the sentiments for text data that does not have a label, without manually assigning polarities to it.
hello sir, there is a doubt am getting somewhat different please help. When i try to implement it the spaces between each word in a sentence is getting removed automatically. i don't know what am doingg wrong please help.(example: Explanation is good ---> Explanationisgood) this is happening need help. Thank you
Hi, I can't fix overfitting for Twitter sentiment analysis. I tried many methods but it didn't work. can you just help? And can you share the code please? Thank you.
Hi, I've updated the link to the code in the video description. github.com/roshancyriacmathew/Twitter-sentiment-analysis-using-Python-Machine-Learning-Project-8 What is the issue with overfitting ?
Can you help me a project that graduated from Sentiment Analysis using Word Sense Disambiguation of word sense disambiguation using Methods (NB, MaxEnt, SVM) and Sentiment Analysis using Deep Learning (CNN, RNN, LSTM) and it doesn't have much information about Python language and project duration It's going to end, please help😭😭😭
The accuracy of the model may vary depending on many factors some of which include, preprocessing steps, number of iterations, system architecture, data used, etc..
Hi, if there is an issue with this section of the code, it might be mainly because of the lemmatizing function that you have defined somewhere earlier in the code. So do check if the lemmatizing function is working. This should solve the issue.
You can use Navie Bayes algorithm for this project. However I have used Logistic Regression and SVC as it best suits the given problem. Instead of using Logistic regression classifier, you can add the code for Naive Bayes (import required libraries) in the same place. I'll be using Navie Bayes classifier in one of my upcoming videos. Thanks - RCM
hey in line 11 , have you mistaken anything ? you have written text_df.text = text_df.apply()... is it corrext ? or should it had been text_df = text_df.apply() ?
Hi... After using your code for stemming, I observed that it wasn't applied as the output showed that the tweets were not stemmed. I would expect that after stemming, the word "bringing" will become "bring". Also how can I remove @mentions with profile names in tweets?
You can try and change the stemmer used in this case to give the required result. For more detailed information regarding the different types of stemmers, you can check out my video on stemming where I explain the other techniques. ua-cam.com/video/UBxM2OyGTXg/v-deo.html text = re.sub(r'\@w+|\#','',text) This line of code in the data preprocessing block will help to remove the @ symbols from the data. Hope that helps.
Hi... I'm back again! 😊 My model majorly misclassified the negative class. I realised it's because Logistic regression performs well in binary classifications whereas most sentiment analysis task are multi-class tasks. Will you say the choice of the model is wrong? Does that show wrong judgement in model selection if one was being assessed?
I'm not sure if I have the reference links for the concepts used in this video as I've built the code on top of a base code that was developed while doing my Master's program.
You can find the code to all my tutorial videos in the description box. github.com/roshancyriacmathew/Twitter-sentiment-analysis-using-Python-Machine-Learning-Project-8
The csv file is avilable on kaggle and I've added the link to the csv file in the video description. www.kaggle.com/datasets/gpreda/pfizer-vaccine-tweets
Can someone help me to solve this Having error in data perprocessing filtered_text = [w for w in text_tokens is not w in stop_words] NameError: name 'w' is not defined
Dear Viewers, a lot of time and effort goes into making these videos. So, all I ask of you is to subscribe to my channel (this would be at no extra cost to you!) and hit the like button if you find the video useful. This would really help my channel. Thanks in advance! - RCM.
which algo is used in this project
@@SIUUfansz logistics Regression and Support Vector Classifier
Can you please how to execute twitter sentiment analysis based on ordinal regression project...
Can u let me know the references u took for this
Hi sir glad to visit your channel. Nice content and i tried your code and it worked as you have shown in the vedio but last some of the codes you are not used in the vedio whats the purpose of the code?? Can you explain me that pls.
Thank you so much. Glad to hear that. Can you specify which part of the code you are referring to?
@@TheAIandDSChannel You showed that codes first in the vedio in that you said for live ones try the same code and what is that u can't understand that one ❤️
@@jeevanasenthilkumar 0:29 shows how to work with live data from Twitter. In order to work with the live data, you need to have a Twitter developer account which gives access to Twitter API from where you can pull the live data. This is a bit time consuming process and hence was avoided in this video.
@@TheAIandDSChannelcan you tell me how to use Twitter api in this
Very informative and Amazing video. Thanks for your efforts and time.
Happy to hear that. 😊
can you please do sentiment analysis using Tensorflow.
Can you please how to execute twitter sentiment analysis based on ordinal regression project...
Can You Please Make video for this? "Automated Classification of Societal Sentiments
on Twitter With Machine Learning"
I want to ask you keyboard shortcut, the way you change the pos_tweets to neg_tweets by selecting only 'pos' word and replacing it with 'neg' word from 16:10 to 16:14 of the video.
You can check out this video where I explain this keyboard shortcut.
ua-cam.com/video/DHN-0tpxYk0/v-deo.html
thank you for the knowledge
Your welcome
Thx for the detailed demonstration! I do feel curious though if you already have a way to generate sentiment score why bother running machine learning on it?
You're Welcome!. We are generating sentiment scores to get labels for the text data. Sometimes this label is provided in the dataset. We then use this text data, and sentiment polarities to train the machine learning model so that we can use the machine learning model to predict the sentiments for text data that does not have a label, without manually assigning polarities to it.
I am suscribed good video
Thank you 😊
Great video. Thank you sir !
You're Welcome. Glad you found it helpful.
hello sir, there is a doubt am getting somewhat different please help. When i try to implement it the spaces between each word in a sentence is getting removed automatically. i don't know what am doingg wrong please help.(example: Explanation is good ---> Explanationisgood) this is happening need help. Thank you
Hi, I can't fix overfitting for Twitter sentiment analysis. I tried many methods but it didn't work. can you just help? And can you share the code please? Thank you.
Hi, I've updated the link to the code in the video description. github.com/roshancyriacmathew/Twitter-sentiment-analysis-using-Python-Machine-Learning-Project-8
What is the issue with overfitting ?
can plz tell me how may classifier using this project bro
Which language used in backend database in this project
Can you help me a project that graduated from Sentiment Analysis using Word Sense Disambiguation of word sense disambiguation using Methods (NB, MaxEnt, SVM) and Sentiment Analysis using Deep Learning (CNN, RNN, LSTM) and it doesn't have much information about Python language and project duration It's going to end, please help😭😭😭
Could please tell the inner functionality of textblob and it's calculations
I think you would find this article helpful. textblob.readthedocs.io/en/dev/quickstart.html
Hi,
I'm using same code still gaetting different
accuracy score then yours
why is it so???
The accuracy of the model may vary depending on many factors some of which include, preprocessing steps, number of iterations, system architecture, data used, etc..
Can anyone please tell what is the logistic regression prediction of the above dataset
Plz requesting to reply to the above question
مرحبا ..كيف يمكنني الحصول على قاعدة البيانات التي تم استخدامها في هذا الفيديو؟
I tthim=nk they are aavailable in kaggle: /pfizer-vaccine-tweets/vaccination_tweets.csv
Hello sir i m facing problem in line
tweet_df['tweet']=tweet_df['tweet']. apply (lambda x: lemmatizing(x))
Pls sir it's imp 🙏
Hi, if there is an issue with this section of the code, it might be mainly because of the lemmatizing function that you have defined somewhere earlier in the code. So do check if the lemmatizing function is working. This should solve the issue.
Getting an error in .fit(x_train,y_train)
Can I get the project with naive Bayes algorithm
Here is another project that I've done using the Naive Bayes algorithm. ua-cam.com/video/nCrxg2FWeTY/v-deo.html
hello, what if i want to use Naive bayes Algorithm how do i do that? thank you so much for your tutorial btw
You can use Navie Bayes algorithm for this project. However I have used Logistic Regression and SVC as it best suits the given problem. Instead of using Logistic regression classifier, you can add the code for Naive Bayes (import required libraries) in the same place. I'll be using Navie Bayes classifier in one of my upcoming videos. Thanks - RCM
Sir, I did import lib textblob, instead I get error as module not found
This might be occurring due to an error while importing the package
@@TheAIandDSChannel Thanks
hey in line 11 , have you mistaken anything ? you have written text_df.text = text_df.apply()... is it corrext ? or should it had been text_df = text_df.apply() ?
text_df is the name of the dataframe. I'm writing text_df.text to apply the function on that particular column.
Hi... After using your code for stemming, I observed that it wasn't applied as the output showed that the tweets were not stemmed. I would expect that after stemming, the word "bringing" will become "bring".
Also how can I remove @mentions with profile names in tweets?
You can try and change the stemmer used in this case to give the required result. For more detailed information regarding the different types of stemmers, you can check out my video on stemming where I explain the other techniques. ua-cam.com/video/UBxM2OyGTXg/v-deo.html
text = re.sub(r'\@w+|\#','',text) This line of code in the data preprocessing block will help to remove the @ symbols from the data. Hope that helps.
Thanks for responding. Did you implement tokenization and part of speech tagging during the pre-processing phase? That's not exactly clear to me.
@@chiomaanyiam1138 Welcome. Tokenization, yes, POS tagging, no. Tokenization was done during the preprocessing step.
Noted, thank you.
Hi... I'm back again! 😊 My model majorly misclassified the negative class. I realised it's because Logistic regression performs well in binary classifications whereas most sentiment analysis task are multi-class tasks. Will you say the choice of the model is wrong? Does that show wrong judgement in model selection if one was being assessed?
hello which algorithm is used in this?
Hi, The classification algorithms used in this video are, logistic regression and support vector classifier.
Bro u have the python code that can be executed in the pycharm
Hi, I have given the python code in different formats *(.py and .ipynb)* in my github link which you can find in the video description box.
@@TheAIandDSChannel ya but .py has no file in it
Bro can u send that .py file
Please check your inbox. I've shared a screenshot of the .py content
Can u let me know the references u took for this
I'm not sure if I have the reference links for the concepts used in this video as I've built the code on top of a base code that was developed while doing my Master's program.
how to create web site for this project
I'm sorry, I don't have much experience with front end (UI) development.
How do you get user_verified bro?
It depends on the dataset, for this dataset, this information was already there.
@@TheAIandDSChannel it means it was inserted manually?
Because I cannot get from Twitter on my dataset
Can u send the CSV file
You can get the link for the CSV file form the following link, www.kaggle.com/datasets/gpreda/pfizer-vaccine-tweets
Can u snd the code
You can find the code to all my tutorial videos in the description box.
github.com/roshancyriacmathew/Twitter-sentiment-analysis-using-Python-Machine-Learning-Project-8
Can I get the csv file
The csv file is avilable on kaggle and I've added the link to the csv file in the video description. www.kaggle.com/datasets/gpreda/pfizer-vaccine-tweets
@@TheAIandDSChannel tq bro
Can someone help me to solve this
Having error in data perprocessing
filtered_text = [w for w in text_tokens is not w in stop_words]
NameError: name 'w' is not defined
us 🤝🤝