yrtnsari/Sentiment-Analysis-NLP-with-Python
The project is a simple sentiment analysis using NLP. The project in written in python with Jupyter notebook. It shows how to do text preprocessing (removing of bad words, stop words, lemmatization, tokenization). It further shows how to save a trained model, and use the model in a real life suitation. The machine learning model used here is k-Nearest Neighbor which is used to build the model. Various performance evaluation techniques are used, and they include confusion matrix, and Scikit-learn libraries classification report which give the accuracy, precision, recall and f1- score preformance of the model. The target values been classified are positive and negative review.
This helps you automatically categorize written feedback or reviews as either 'positive' or 'negative.' It takes raw text data as input and provides a trained model that can classify new text, along with metrics showing how well the classification works. This is useful for anyone who needs to quickly gauge public opinion or customer sentiment from a large volume of text.
No commits in the last 6 months.
Use this if you need a straightforward way to understand the overall tone of customer comments, social media posts, or survey responses.
Not ideal if you require more nuanced sentiment classification beyond just positive or negative, or if you're dealing with very short, informal text like tweets where context is crucial.
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Jupyter Notebook
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Last pushed
Mar 15, 2022
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