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The project utilizes Beautiful Soup, a Python library, for web scraping Reddit posts.
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Multithreading is employed to scrape posts from Reddit in parallel. This approach enhances efficiency by allowing multiple threads to execute simultaneously, thereby reducing the overall time required for data collection.
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Natural Language Processing (NLP) techniques are applied for sentiment analysis. This involves tokenizing comments and utilizing dictionaries of positive and negative words to determine the sentiment of each comment. NLTK, a popular Python library for NLP, is used for these tasks.
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After extracting and processing the data, various analyses can be performed. This may include aggregating statistics, identifying trends, or conducting further analysis to gain insights into the content and sentiment of Reddit posts and comments.
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SQL (Structured Query Language) is used for storing and retrieving the processed data. This allows for efficient data management, querying, and analysis. It enables users to perform complex queries to extract specific information or insights from the dataset.
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By integrating these components, the project creates a comprehensive pipeline for gathering, analyzing, and storing Reddit data. It leverages the strengths of each technique and tool to efficiently handle large volumes of data and derive valuable insights from it.
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This project includes Web scraping of posts in parallel using multithreading off of Reddit using python library Beautiful Soup, processing and doing Sentiment Analysis on it using NLP and further analyzing the data using SQL.
farazkhancodes/NLP-Webscraping-Multiprocessing-with-SQL-Python
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This project includes Web scraping of posts in parallel using multithreading off of Reddit using python library Beautiful Soup, processing and doing Sentiment Analysis on it using NLP and further analyzing the data using SQL.
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