In this project we should follow the instructions carefully because this is a brief introduction to NLP and how to deal with semtniment analysis.
Of course, we'll handle it with Naive Bayes.
We'll have the opportunity to go deeper with sentiment analysis and NLP in its own future lesson.
You will not be forking this time, please take some time to read this instructions:
Once you are finished creating your naive bayes model, make sure to commit your changes, push to your repository and go to 4Geeks.com to upload the repository link.
Naive sentiment analysis
This is a simple project using Naive Bayes Classifier and Scikit-learn to create a Google Play store reviews classifier (Sentiment Analysis) in Python. You will categorize user reviews as good or bad. The Naive Bayes classification technique is a simple and powerful classification task in machine learning. The Naive Bayes classification technique is a simple and powerful classification task in machine learning. In this dataset, we use the 23 most popular mobile apps, and only two columns.
We have three columns: package name, review and polarity (0 = bad, 1 = good) Preprocess the data by eliminating the package name column and putting all reviews in lower case.
Separate target from feature, and split your data.
Vectorize your features and use Naive Bayes to classify the reviews as good or bad. We will not focus on hypertuning our model this time. This was an introduction project to sentiment analysis using Naive Bayes.
Use the app.py to create your pipeline.
Save your naive bayes classification model in the 'models' folder.
In your README file write a brief summary.