Build + Deploy a Sentiment Analysis Model(on Heroku using flask) to classify YouTube comments for brand image improvement
An end-to-end toolkit on building a sentiment prediction model with a Jupyer notebook and deploying model on Heroku using flask. Our use case here is review classification of YouTube comments on Samsung Galaxy S23 Ultra into positive, neutral & negative and suggest moddifications to the product to improve brand image of Samsung. I have scraped comments using youtube_comment_scraper_python library by bot-studio.
Sentiment Analysis ISI Chennai 2023
I suggest the following modifications to ”Sumsung Galaxy S23 Ultra” developing team based on the negative comments recieved
• To include expandable memory card slot for upcoming version. • To use the better processor for upcoming version. • To include more features than previous model for upcoming model.
• Scraping comments on Samsung Galaxy S23 Ultra's official film available on YouTube using Python • Labelling data as positive or negative based on polarity scores • Transforming data: Using lemmatization module; converting alphabets to lower case; removing new lines, punctuations, special characters, stop words; tokenization • Encoding: Labels to numerical form; comments to vectors using TfidfVectorizer • Balancing the dataset • Machine Learning model: Splitting the data into train and test set, feeding the data to Support Vector Classifier • Model pipeline: Creating a pipeline which passes the data first into TfidfVectorizer and then to SVClassifier • Model evaluation using accuracy score • Saving the model pipeline as pickle file • Making index.html and flask app(app.py) • Committing the files to Github • Creating an app on Heroku and connecting it to Github repository • Model deployment on Heroku
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Prerequisites: Python 3.9 (ensure Python is added to PATH) + Git Client
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Open GIT CMD >> navigate to working directory >> Clone this Github Repo
git clone https://github.com/abdulwahed98/NLP-YouTube-Comments-Sentiment-Analysis.git
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Open Windows Powershell >> navigate to new working directory (cloned repo folder)
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Create a virtual environment
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install virtual environment
pip install virtualenv
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create virtual environment by the name ENV
virtualenv ENV
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activate ENV
.\ENV\Scripts\activate
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Install project dependencies
pip install -r .\requirements.txt
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Run the project
python app.py
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Look for the local host address on Powershell screen, something like: 127.0.0.1:5000 >> Type it on your Web Browser >> Project shall load
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Try out your Amazon Alexa test reviews and look for results
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To close >> Go back to Powershell & type
ctrl+c
>> Deactivate Virtual Environment ENVdeactivate
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Prerequisites: Python 3.9
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Open Terminal >> navigate to working directory >> Clone this Github Repo
git clone https://github.com/abdulwahed98/NLP-YouTube-Comments-Sentiment-Analysis.git
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Navigate to new working directory (cloned repo folder)
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Create a virtual environment
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install virtual environment
pip install virtualenv
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create virtual environment by the name ENV
virtualenv ENV
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activate ENV
source ENV/bin/activate
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Install project dependencies
pip install -r requirements.txt
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Run the project
python app.py
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Look for the local host address on Terminal screen, something like: 127.0.0.1:5000 >> Type it on your Web Browser >> Project shall load
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Try out your Amazon Alexa test reviews and look for results
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To close >> Go back to Terminal & type
ctrl+c
>> Deactivate Virtual Environment ENVdeactivate