spaCy-based CLI for web linguistic analysis with embeddings, sentiment, POS/NER, and Unix pipeline composability. Outputs JSON, Parquet, CoNLL-U for ML workflows.
This project leverages Natural Language Processing (NLP) techniques to analyze the sentiment expressed in web content, providing insights into overall sentiment polarity, subjectivity, and readability of overall content.
Text extraction from URLs and NLP-based sentiment analysis using Python, BeautifulSoup, and NLTK
Python-based tool for scraping web essays and articles, performing dictionary-driven sentiment analysis, and computing readability and linguistic metrics. Converts qualitative text into structured quantitative insights with Excel-based input/output.
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