Natural Language Processing with Python

This repository contains code and resources for Natural Language Processing (NLP) with Python. It includes code examples, notebooks, and datasets that demonstrate various NLP techniques, such as text classification, sentiment analysis, named entity recognition, and topic modeling.

Table of Contents

1. Installation
2. Usage
3. Notebooks
4. Datasets
5. Concept CLearance
6. Conclusion

Installation

To use the code in this repository, you'll need to have Python 3.x installed on your machine. You can download Python from the official website:
https://www.python.org/downloads/

In addition, you'll need to install the following Python libraries:

1. NLTK
2. scikit-learn
3. spaCy
4. gensim

You can install these libraries by running the following command in your terminal:

pip install nltk scikit-learn spacy gensim

Usage

To use the code in this repository, you can clone the repository to your local machine using the following command:

git clone https://github.com/your_username/nlp-with-python.git

Notebooks

1. Text Preprocessing: This notebook covers the main steps of text preprocessing, including lowercasing, tokenization, stopword removal, stemming and lemmatization.
2. Exploring Text Data: In this notebook, we'll see some basic techniques to explore text data, such as word frequency analysis, word clouds and sentiment analysis.
3. Bag-of-Words Model: This notebook explains the bag-of-words model, a simple yet powerful representation of text that allows us to apply machine learning algorithms. We'll cover how to build a bag-of-words matrix, how to handle vocabulary size and how to represent documents as vectors.
4. Algorithms: This notebook presents the Naive Bayes algorithm, a simple and effective method to classify text documents. We'll see how to train a Naive Bayes classifier and SVM classifier on a text dataset and how to evaluate its performance.
5. Word Embeddings: This notebook introduces word embeddings, a more advanced representation of text that can capture semantic relationships between words. We'll cover how to train and use word embeddings with the popular Word2Vec algorithm.

and many more!

Data

The notebooks use several datasets that are available in the data folder. These datasets include:

1. Movie Reviews: A dataset of movie reviews labeled as positive or negative.
2. Twitter Sentiment: A dataset of tweets labeled as positive, negative or neutral.
3. BBC News: A dataset of news articles from five categories: business, entertainment, politics, sport and tech.
4. Song Lyrics: A dataset of song lyrics from four artists: Eminem, Beatles, Taylor Swift and Queen.

Concept Clearance

If you are new to NLP or need a refresher on key concepts, we recommend reviewing the "Introduction to NLP" notebook before diving into the other notebooks. Additionally, the following terms and concepts are helpful to understand before working with NLP:

1. Tokenization: The process of splitting text into individual words or tokens.
2. Stop words: Common words that are often removed from text during preprocessing because they do not carry much meaning (e.g., "the", "a", "an").
3. Stemming: The process of reducing a word to its root form (e.g., "jumping" becomes "jump").
4. Lemmatization: The process of reducing a word to its base form (e.g., "jumping" becomes "jump").
5. Bag of Words: A representation of text data that involves counting the frequency of each word in a document or corpus.
6. TF-IDF: A method for weighting words in a bag-of-words representation based on their frequency in the document or corpus.

Contributions

We welcome contributions to this repository! If you have a notebook you would like to add, please submit a pull request. Additionally, if you notice an error in one of the notebooks or have suggestions for improving the content, please create an issue.

Getting Started

To get started, simply clone or download the repository and run the notebooks in your favorite environment. You can follow the notebooks in order, or pick the ones that interest you the most. Have fun exploring NLP!