An end-to-end toolkit on building a sentiment prediction model with a Jupyer notebook and deploying model pickle on local machine using flask. Our use case here is review classification of Amazon Alexa customer feedbacks into positive and negative. Dataset source is here.
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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/deathmukh/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
-
Open Terminal >> navigate to working directory >> Clone this Github Repo
git clone https://github.com/deathmukh/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
-
Try out your Amazon Alexa test reviews and look for results
-
To close >> Go back to Terminal & type
ctrl+c
>> Deactivate Virtual Environment ENVdeactivate