Text Processing and Analysis Project

This project involves the collection, preprocessing, and analysis of textual data from various sources, including Amazon, Twitter, and Rotten Tomatoes. The data processing pipelines are designed to clean, preprocess, and transform raw text into structured data for analysis using Python and several powerful libraries.

Project Description

The aim of this project is to automate the fetching and analysis of textual data from multiple sources, clean and preprocess the raw text, and transform it into structured data for analysis. The project employs tokenization, stemming, and stop word removal for data normalization.

Technologies Used

• Programming Languages: Python
• Libraries:
  • NLTK
  • scikit-learn
  • pandas
  • matplotlib
  • seaborn
  • tweepy (for Twitter API)
• APIs:
  • Twitter API
  • Facebook Graph API
• Databases:
  • SQL or NoSQL database (optional for storing retrieved data)

Features

Data Collection

• Automatic Data Fetching: Implemented data collection scripts using Python libraries (NLTK, Tweepy) to fetch textual data from various sources including Amazon, Twitter, and Rotten Tomatoes.

Data Processing Pipelines

• Cleaning and Preprocessing: Engineered data processing pipelines to clean and preprocess raw text.
  • Tokenization: Splitting text into individual tokens (words or phrases).
  • Stemming: Reducing words to their base or root form.
  • Stop Word Removal: Removing common words that do not contribute to the analysis.

Data Analysis

• Structured Data: Transforming preprocessed text into structured data suitable for analysis.
• Visualization: Using matplotlib and seaborn to visualize data and analysis results.

Installation and Setup

To run the project locally, follow these steps:

1. Clone the Repository:

   git clone https://github.com/your-username/text-processing-analysis.git
   cd text-processing-analysis