This case study explores the application of text classification in the context of NLP projects. Text classification is a fundamental task in natural language processing that involves categorizing text into predefined classes or categories.
Overview
Text classification is widely used in various domains such as sentiment analysis, spam detection, and topic classification. In this case study, we will focus on a text classification project that involves classifying customer reviews into positive, negative, or neutral categories.
Dataset
The dataset used for this project consists of customer reviews from various sources. The reviews are preprocessed and tokenized to facilitate the classification task.
Preprocessing
The preprocessing step involves cleaning the text data and preparing it for the classification model. This includes:
- Removing stop words
- Lemmatization
- Vectorization
Model
For this project, we have used a Naive Bayes classifier, which is a popular algorithm for text classification tasks. The Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
Evaluation
The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The model achieves an accuracy of 85% on the test dataset.
Conclusion
This case study demonstrates the application of text classification in the context of NLP projects. The project achieved an accuracy of 85% on the test dataset, which is a promising result for further improvements.
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