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.
spaCy-based CLI for web linguistic analysis with embeddings, sentiment, POS/NER, and Unix pipeline composability. Outputs JSON, Parquet, CoNLL-U for ML workflows.
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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