Hotel Reviews Sentiment Analysis In python|NLP Sentiment analysis in Python
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- Опубліковано 27 сер 2024
- Hotel Reviews Sentiment Analysis In python|NLP Sentiment analysis in Python
#SentimentAnalysisInPython #NLPSentimentAnalysis #UnfoldDataScience
Hi ,
This is Aman and I am a data scientist.
About this video:
This video is about sentiment analysis of hotel reviews in python. Below questions are answered in this video:
1. How to do sentiment analysis in python
2. What is Sentiment analysis in python
3. Text Sentiment analysis in python
4. Sentiment analysis using python for text data
5. NLP sentiment analysis in python
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Very well explained in short and concise manner. Didn’t knew about the pipeline module. Learnt an extra thing along with NLP implementation.
Welcome Siddhesh.
Your videos are so straight to the point, the concept is nailed so very well, that it is registered in mind at once. Thanks!
Thanks Vishal. Your comments are precious for me.
I dont usually like or comment any video but after going through yours i cant resist myself to appreciate your efforts.
Short and sweet... very good work... keep it up...
Thanks a lot Nevil. Your comments motivate me.
Please share within your data science groups if you find it useful.
The reason for the last incorrect prediction is that 'not' and 'happy' are considered as separate words. Hence, the word 'happy' has not been negated which is expected. If we consider 2-gram then it might improve the prediction. Amazing video. You just got a subscriber. Keep on Unfolding Data Science.
Thanks a lot. When audience gets involved with the content, that's real happiness for me.
@@UnfoldDataScience do you have any lessons on how to implement 2 gram
Thank you very much and the way you teach us is mind blowing.
Welcome Akash.
Really helpful
Thanks again Vishnu.
Thanks a lot, man, this video helps me so much for my project!
Thana for watching.
thanks...nice and simply explained
Welcome Akash.
Thanks a lot Aman. I would request you to please come up with more such tutorials (if your time permits) wherein you can show your thought process behind ML models that you create with kaggle datasets or for kaggle competitions. I am requesting this to you, as I think your videos is helping me a lot in learning and developing concepts in data science. Thanks Again😊😊
Yes, definitely Anirban.
We can use Negation handling and BERT technique like word flipping for to avoid "not","never" ...
Thanks for suggesting.
Bro why did you not use stop word removal, lemmatization and stemming?
Other than that, a really good video.
Thank you for this.
Thanks prince for your feedback. Tf idf does few things internally but yes advisable to do by self. Happy learning, stay safe!!
No doubt, very clear and nice...
Glad you think so Dipannita.
Yo! Nice tutorial bro!
And thanks for sharing your code.
Make more videos on a similar concept.
Respect!
Glad It was helpful. Happy Learning Vladimir.
Can you give me the this jupiter notebook file?
Awesome video, I am doing project on sentiment analysis it helped me alot.
Glad it helped Salik.
Here you did cleaning and feature engineering first then train test split but in the recent video you said we should first do train test split then all the cleaning and FE to protect from Data leakage ?
Just to save time, Ideally, break first then do anything.
Very good, excellently presented. I wish you could have explained behind the scene activities of vectorizer and logistic classifier. I like it!
Noted. Thank you
When u do cleaning of description column using function, how does it exactly take the description column??
Thank you,its best video.I have one question for you how i can compare more than one text vectors by using cosine similarity ,and display the cosine score in pd.dataframe.
Hi Yonas, use the cosine similarity formula from python
Very nice AMan
Thank you 😃
Nice video and nice work ,well-done Please can you make video on sentiment analysis on suicidal ideation(classification) using twitter data
Thanks. Similar concept can be applied for your problem statement as well.
@@UnfoldDataScience oh that is good, thanks man
Good one!
Thank you Prerana :)
FINISHED WATCHING
Can you tell me which algorithms and which libraries are used in this project?
Please let me know why didn't you do Tokenization and Stemming/Lemmatization in this project even though it is an NLP proj? Thanks in advance :)
Here just to keep video short, j may have skipped but it's good practice to do it always.
What it means when length of test, train & split comes (1,1,1) ? how to fix that. Please help.
Hello, thank you for your video and for sharing your knowledge
Can you please explain why use a random state of 225 ? why not only put 1 ?
Random state is just a seed. You can put 1 as well.
Hi Aman - I have started this playlist today and I am highly impressed with the way you explain things.
I have one query.. We have text data as well as corresponding ratings on websites.We use this data as training data.
So why we need sentiment analysis tool??
For which data we need predictions, if we already have text & ratings on every website.
Please respond.
Hi Nikhil, this is a good question. In this example, we have the rating however we might not have rating in all real world use case.
We create rating based on our business understanding and then use it to train model.
Later, we can use trained model for rating prediction for new examples.
@@UnfoldDataScience Thank You Aman for responding. As per my limited knowledge I can only see that in every field we have ratings data such as retail, movie, songs, even after customer care call we give ratings.
Still not clear on which data a company need to predict ratings.
Its a request if you can answer me or provide some use case on my email nikhilgpt23@gmail.com.
I have completed your timeseries playlist as well. Will post my questions there. Superb work!!
@@nikhilgupta4859 hii..I also had the same qsn...did u get the answer of it?
@@nishah4058 No I haven't got any response..If you get it please post it here as well :)
@@nikhilgupta4859 I get to know by thinking on it... actually it's for hotels chain(who own the hotel) in case of hotel review that what is their market reputation...how customers like their services etc...if the company itself read no of reviews by itself which can't be practical so there is a need of sentiment analysis when one business or organisation want to know their reputation,why they are lacking with others,what they can improve in their system...this is the main reason to do sentiment analysis.i think so
Very nice presentation..
Thank you so much :)
plz give the roadmap of data science?
Thanks Aman for the wonderful explaination !
One question to ask, why did you use 'lbfgs' within Logistic model not 'newton-cg' or any other method?
Thanks Shailendra. Just like that nothing specific.
Thanks.easily explained.
Thank you Pavan. Happy Learning!
I'm complete ur task..
If you are increasing train dataset..
" I'm not happy" this will give u "not happy " result.
Hi Arjunram, Even it it gives, the approach that we took in this analysis is a basic one. Our solution should take care of many things in real world applications, hence better approach should be taken.
I am very happy that you tried the use case and coming up with queries. Happy Learning. Tc
Hi Great Explanation! Any update for "I am not happy?" such kind of reviews?
Very good sir
Thanks Ashok, Keep watching. Happy learning. stay Safe!
I think using a neural network for the same can improve accuracy. Can you make a video for that?
HI Ray, thaks for your feedback. Yes definitely it can help. I will create a video on this topic using same data and check.
@@UnfoldDataScience Yea will surely love to see from you video on this because you are such a great teacher, besides with neural network we can get such a great result in terms of accuracy
kindly provide the link next part of this video where u solved this problem of strong word ..
Is it not in playlist?
i can't find this code in your drive. there is a code in npl hybrid model which is related to semtiment analysis but the dataset you used in that is a customer review from a flight.
It should be there in NLP folder
no its not there plzz upload it
@@UnfoldDataScience
Hello sir, why didn't you apply steaming and tokenization ?
Read previous comments, I clarified
Very nice
Thank you.
Why didn't you change the independent variables to numbers eg 0 and 1
The link doesn’t have this file, please update the link, i am waiting
Thank you very much for the video!Which algorithm have you used for sentiment analysis here?
You are welcome, I have created data using TF-IDF and then use classification algorithm :)
@@UnfoldDataScience hello sir can you please tell me which classification algorithm you have used in it?
I don't think you used TF-IDF function, hence, happy was not masked very well in the data
could you please make video on project of analysing google play store using LDA for text mining and sentiment analysis? As I work on same project and face some problems in applying
What if I have a dataset which only has reviews and not description. For example, how do I go about doing sentiment analysis when I am given only the reviews in the dataset.
You can create a label by yourself for a subset of reviews and use it for model training.
we can use n grams
Very well explained. Thank you. I am just wondering that how can perform the operations in python by triggering it from my angular project and display result there. May I seek assistance on it please?
Thank you Sir.
Nice video
Thanks Vinay, stay safe. Happy Learning.
How to get the Is_Response===> happy or unhappy in the dataset if that doesn't predefined in dataset already
Good question, we need to create a target varibale based on many things. sometimes manual process
can you make this for multiple languages? btw this tutorial was awesome
Yes u need translator
Hello u said applying sentiment analysis but u applied only TFidf vector alone
Hi Dhilip, yes good point. I have not used any existing packages but derived features from text and then training model.
in the google drive i cannot find the code of this sentiment analysis using logistic Regression. please specify me the path
Hi Aman, Is there a way to handle "NOT GOOD" as "BAD". i.e negation of words.
Yes Abhijit, that is where "context based " learning comes into picture.
Bro I don't know location of file to download hotel review project , please help me, today I want to submit my project in my clg
drive.google.com/drive/folders/1XdPbyAc9iWml0fPPNX91Yq3BRwkZAG2M
which algorithm is used?
In NLP classification problem sampling is required
please do more videos on NLP . thank you
Sure Harini.thanks :)
Hello, can you explain the reason behind, why we are getting as happy result on providing the review as "I'm not happy".
I have seen in the comments, better approach should be taken, can you please explain which is the better approach.
Hi Shravan, that is a very good question. What i have demonstrated here is a very simple approach for NLP problems. This is not what is recommended for real world use cases. Here we are taking "frequency based approach" where the semantic meaning of word is sentence is not captured hence model gives result unexpected way as the example you gave.
Better approach will be, using Word embeddings/Prediction based models/sequence based Deep learning models where we can capture sematic meaning of a sentence.
Sir why you won't use tokenization and lemmatization in the preprocessing step?🤔
Can you guide?
Hi Yagya, here I might not have used however those are recommended way of cleaning text data. good point noticed.
sir which algorithms did you used here
Hello Triveni, it's TFIdf/count vectorizer.
Please make a video on hybrid features we have a BE project - sentimental analysis using hybrid feature
Thanks Akshay for suggesting.
i am not able to access to data set can u attach a link
Why didn't you balance the label data?
Good question, it should be done. Here in this demo I did not follow all steps as I wanted to demonstrate things at high level.
@@UnfoldDataScience ok please tell the technique which can be used to balance text data/in nlp
You should have to handle negation scope to improvise accuracy
Yes Misbah, good requestion
from which file this project is saved?
I think to improve we can go for bi-gram or tri-gram to get better understanding of the concept
Yes we can try :)
Can we use countvectorizer instead of TfidfVectorizer
Hi Anand. yes we can.
hello sir,i got this error AttributeError: 'DataFrame' object has no attribute 'Is_Response' when i am doing this
check column names properly
@@UnfoldDataScience all values are same but it's not working
how to display model performance metrics
Hi Faiza, same way like TPR/FPR etc
The system understand the meaning of "Don't" but Don't understand the meaning "do not". I find that reason. The maim fact is Don't is represent as a single word which obtained a single index in vector but do not split out in vector and abstain separate index. That's why that kind of problem arise
Very true. Good catch.
my session crashed (utilized all the RAM) after the confusion matrix line, is there any particular reason ? how do i resolve the issue ?
Hi Anurag, can u share the screenshot in email?
@@UnfoldDataScience I have shared the code to this email - amanrai77@gmail.com
Will help me to know sentence classification using rnn
sure
got an error: AttributeError: 'Series' object has no attribute 'lower' while doing the first layer of cleaning
Greetings Sir, do you still take questions?
I do.
Cant we do the same thing with ML ??
It is ML only. The only difference is how we extract features from text.
amazing explaination, whatever you explain it got stuck to my mind. Thank you sir ,go hard
You are most welcome Yogesh.
Getting an error "'Series' object has no attribute 'lower'
" line 34, in text_clean_1
text = text.lower().
Datatype issue. Chane it to string.
Hey, great video. Can you please upload this code file in your google drive? there is not in your google drive.
drive.google.com/drive/folders/1XdPbyAc9iWml0fPPNX91Yq3BRwkZAG2M
Hi, Please I am carrying out a research on online reviews for hotels. Please how do I pull these data for data collection, since copy and paste wouldn't work? Thanks.
Hi, you need to use some web scrapping method. Take this code as reference and change based on your need.
@@UnfoldDataScience Hello, thank you for your response. Please I cannot find the code. Kindly assist.
Can we get over all project to download link sir....
Inn description
great video sir
can you give me the link of kaggle dataset
drive.google.com/drive/folders/1XdPbyAc9iWml0fPPNX91Yq3BRwkZAG2M
What is the next level for research work...in this sir?
Hi Janaki, many research work is going on, Join me live sunday 4PM IST, we can discuss mre.
How to check the real and fake reviews
where i can get train dataset
check description
Sir, need some guidance on NLP.
Yes Sure. Write me in email the details about it.
Sir can we do this for regional language?
Ys, u need translator
⭕️⭕️❌❌‼️‼️5:03 I am getting Empty in column of cleaned description AFTER applying text clean 1‼️‼️‼️⁉️⁉️⭕️⭕️🚫🚫❌❌
This can be a python issue
I am facing same problem. (I am not good) give me positive sentiment in my project. How to solve it?
Thank you
That is exactly where sequence based models like LSTM comes into picture :)
Hello sir... Is it possible to scrap data from google play store app reviews
Not sure of a direct way.
but is it necessary to create a new column?
I am confused
Which part of the video you are talking Salik? Can u point me pls.
@@UnfoldDataScience you create a new column after cleaning the text. that's what I am talking about...
how to apply here SVM?
SVM can be used for classifier.
can you please reply with an exact answer...if possible.
i have around 27000 customer reviews. but I don't have ratings for those reviews.
i need to generate ratings or sentiment(+ve or -ve) for each review. or I need to classify those reviews.
how would I do...
is there any possibility to cluster those reviews into two clusters? and consider as +ve and -ve reviews. if yes please describe the approach.
if not..please.
describe your approach.
thanks.
Answered in email :)
@@UnfoldDataScience can you share the with me also
@@ANKITSINGH-fv1ow first apply unsupervised technic to the problem, like kmeans or hierarchical, we can then train our model on that.
How to change is_response to numbers as 0 or 1?
Hi Rohit , If you want to change, you can do something like this
df.loc[df["is_response"]== "positive", "is_response"] = 1
@@UnfoldDataScience I did it through get_dummies() anyways thanks for replying 🙏
Can we get the dataset and code ? It will be very helpful for us
drive.google.com/drive/folders/1XdPbyAc9iWml0fPPNX91Yq3BRwkZAG2M
its not there
@@UnfoldDataScience
Hi Aman,
Can you please explain what all are getting removed because of the below line?
text = re.sub('\[.*?\]', '', text)
actually I am confused with the .*? part in the expression. What are they used for in this expression?
. * means anything. Bigger bracket means any of the existing thing inside the bigger bracket. For example [ABC] means if string has either A or B or C.
@@UnfoldDataScience Thank you Aman for your reply. Okay so here the target is not to just remove the square brackets ('[]') but also to remove whatever is inside those brackets, is it so?
Like for e.g. txt = ab[egg]ii, will become 'abii', we don't want 'abeggii'
But still I have a small confusion, like here,
text = re.sub('\[.*?\]', '', text)
won't it be sufficient to add '.*' inside brackets. Why we are adding '?'
too?
What if we would have written
text = re.sub('\[.*\]', '', text).
Please help me to find out if I went wrong or missed out the real meaning of this statement somewhere.
help me i want to use naive bayes algorithm and multinomialNB too!!!! how can i implement in it?
Hi Nikhil, both of these can be applied using python package in a similar way. try from your side, if unable to do, let me know. Thanks. tc
@@UnfoldDataScience SIR i've tried but it is always showing error
please help
@@nikhilgahlaut3849 did you succeed on implementing native Bayes?
Can anyone give me this jupiter notebook file?
You can go to "about" Section in my UA-cam channel, scroll down , you will get the link
Can we get link to download plz
Link in description
how to give path of downloaded dataset?
drive.google.com/drive/folders/1XdPbyAc9iWml0fPPNX91Yq3BRwkZAG2M