NLP-Toolkit-App

Overview

The NLP Toolkit is a Streamlit-based web application for performing basic text processing and analysis using Python. It leverages NLTK, SpaCy, and other NLP libraries to provide features such as tokenization, stemming, lemmatization, POS tagging, n-grams, Bag-of-Words, TF-IDF, parsing, Named Entity Recognition (NER), sentiment analysis, and visualization.

Features

• Tokenization (words and sentences)
• Stopwords removal
• Stemming (Porter, Lancaster, Snowball)
• Lemmatization
• POS tagging
• N-grams generation
• Bag-of-Words representation
• TF-IDF representation
• Dependency parsing
• Named Entity Recognition (NER)
• Sentiment analysis
• Word frequency plots and WordClouds
• Download original and processed tokens

Installation

1. Clone the repository:

git clone <repository_url>
cd <repository_folder>

2. Create a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate   # Linux/Mac
venv\Scripts\activate      # Windows

3. Install dependencies:

pip install -r requirements.txt

4. Download the SpaCy English model:

python -m spacy download en_core_web_sm

Usage

1. Run the Streamlit app:

streamlit run app.py

2. In the sidebar, choose your input source:

   • Paste text
   • Upload .txt or .csv file (text should be in the first column)

3. Select the desired NLP options and parameters:

   • Tokenization
   • Stopwords removal
   • Stemming / Lemmatization
   • POS tagging
   • N-grams
   • Bag-of-Words / TF-IDF
   • Parsing / NER
   • Sentiment analysis

4. Click Run NLP to process the text.

5. Visualizations and downloadable token files are available after processing.

Requirements

See requirements.txt for all dependencies.

NLTK Data

The app automatically downloads required NLTK datasets if not present:

• punkt
• averaged_perceptron_tagger
• wordnet
• omw-1.4
• stopwords

License

MIT License