Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{169943,
        author = {Vallluri Jaya Venkata Sai and Shaik Khaja and Koothuru Rohan and G Chandra Shaker},
        title = {SENTIMENT ANALYSIS USING PYTHON},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2755-2760},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169943},
        abstract = {The project involves implementing sentiment analysis in Python aimed at assessing the emotional tone from textual data. This therefore gives good insight about public opinion and emotional trends. Three main categories are categorized through text classification: positive, negative, or neutral sentiments by using libraries such as NLTK (Natural Language Toolkit) and TextBlob.  Key steps involved in the process are data collection, preprocessing which includes tokenization, stopword removal, and lemmatization, and machine learning algorithms that improve prediction accuracy. 
Practical examples depict the ability of sentiment analysis to be applied in any industry, such as social media monitoring where it measures public disposition towards brands or events, customer feedback analysis that assists businesses in knowing consumer satisfaction; and market research in which it gives crucial information concerning consumer preferences. The project showed the capabilities of Python in turning unstructured text into actionable insights; this stressed the growing importance of sentiment analysis in data-driven decisions.},
        keywords = {},
        month = {November},
        }