• Sharan H Amin
• Paramesh D K
• Yogesh S
• Sudeepa S G
• Prof .Reshma B

This paper presents a text analysis system designed to extract key insights from textual data using natural language processing techniques. Traditional methods of manual text review are time-consuming and prone to inconsistency. Our proposed system automates the analysis process, performing tasks such as word frequency calculation, sentiment evaluation, keyword extraction, and summarization. Developed using Python and libraries like NLTK and TextBlob, the tool provides users with meaningful linguistic insights in a user-friendly interface. The system is adaptable to various use cases including academic research, customer feedback analysis, and content categorization. Results show the system’s potential to streamline decision-making by transforming raw text into actionable information.

Text Analysis, Natural Language Processing, NLTK, TextBlob, Sentiment Analysis, Keyword Extraction, Summarization, Python Automation

"TEXT ANALYSER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.207-212, July-2025, Available :http://www.jetir.org/papers/JETIRGX06038.pdf

"TEXT ANALYSER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp207-212, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06038.pdf