AI Councel Lab

  In the world of data science, text analytics plays a crucial role in deriving insights from large volumes of unstructured text data. Whether you're analyzing customer feedback, social media posts, or web articles, natural language processing (NLP) can help you extract meaningful information. One interesting challenge in text analysis involves handling HTML content, extracting meaningful text, and performing sentiment analysis based on predefined positive and negative word lists. In this blog post, we will dive into how to use Python and NLP techniques to analyze text data from HTML links, filter out stop words, and calculate various metrics such as positive/negative ratings, article length, and average sentence length. Prerequisites To follow along with the examples in this article, you need to have the following Python packages installed: requests (to fetch HTML content) beautifulsoup4 (for parsing HTML) nltk (for natural language processing tasks) re (for regular exp...