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This Project Explains the Basic Text Preprocessing and How to Build a Classification Model in Python
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Business Overview
Have you ever wondered how the machine deals with the text we write, the sentence we speak? How the machine takes essential decisions based on just the text? Natural Language Processing, often abbreviated as NLP, gives the ability to machines to understand, read, and get meaningful insights from human language. Basically, NLP is the automatic handling of human languages. Nowadays, NLP is prospering due to the large availability of data and computational power. NLP has dug down its routes from healthcare, media, finance to human resources. It is growing with each coming day.
In this series of projects, we will introduce NLP and associated techniques in a very lucid manner. This project aims to give you a brief overview of text preprocessing and building a binary classification model on processed data.
Aim
To understand the basic text preprocessing and build a classification model.
Data Description
The dataset contains more than a thousand reviews about an application openly available to the public. The data includes reviews and sentiment, i.e., is the review positive or negative with various other variables.
Tech Stack
Language: Python
Libraries: pandas, seaborn, matplotlib, sklearn, nltk
Approach
Data Description and visualization
Introduction to NLTK library
Data Preprocessing
Conversion to lower case
Tokenization
Stopwords removal
Punctuation removal
Stemming
Bag of Words
Binary
Non-binary
N-grams
TF-IDF
Model Building and Accuracy
Predictions on new reviews
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