Natural-Language-Processing-NLP

Welcome to the NLP (Natural Language Processing) Repository! This repository contains code and resources related to various NLP tasks.

NLP Tasks Covered

1. Tokenization: Tokenization is the process of breaking text into individual words or tokens. In this repository, you can find code examples for tokenizing sentences into words using the NLTK library.

2. Part-of-Speech (POS) Tagging: POS tagging is the task of labeling the words in a sentence with their appropriate part of speech.

3. Stemming: Stemming is the process of reducing words to their base or root form. Code examples for stemming words using the NLTK library can be found in the repository.

4. Lemmatization: Lemmatization is the process of reducing words to their base or dictionary form (lemma). Code examples for lemmatizing words using the NLTK library are provided.

5. Bag of Words (BoW): The Bag of Words model is a text representation technique that converts text documents into numerical feature vectors. You can find code examples to create a Bag of Words representation of your text data.

6. TF-IDF (Term Frequency-Inverse Document Frequency): TF-IDF is a numerical representation of the importance of each word in a document relative to a collection of documents. Code examples for calculating TF-IDF scores are included.

7. Word2Vec: Word2Vec is a popular word embedding technique that represents words in a continuous vector space. Code examples for training a Word2Vec model on your text data can be found in the repository.

8. Sentiment Analysis: Sentiment Analysis is the process of determining the sentiment or emotion of a piece of text.

Folder Structure

The repository is organized into the following folders:

• tokenization: Contains code examples for tokenization using the NLTK library.
• POS Tagging: Contains code examples for POS tagging using the NLTK library which contains averaged_perceptron_tagger.
• stemming: Contains code examples for stemming words using the NLTK library.
• lemmatization: Contains code examples for lemmatizing words using the NLTK library.
• bag_of_words: Contains code examples for creating a Bag of Words representation.
• tf_idf: Contains code examples for calculating TF-IDF scores.
• word2vec: Contains code examples for training a Word2Vec model.
• Sentiment Analysis: Contains code examples for training a sentiment analysis model with the help of VADER and TextBLOB Library.

Each folder includes Python scripts or Jupyter Notebooks with detailed explanations and examples for the respective NLP task.

Requirements

To run the code examples in this repository, you need to have the following dependencies installed:

• Python (>= 3.6)
• NLTK
• Gensim
• TextBLOB
• Scikit-learn

You can install the required packages using pip: