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@article{176925,
        author = {Vinita Choudhary and Shrikrishna Balwante and Dhanashree Pogade},
        title = {Beyond Sentiment: A Deep Dive Into Advanced Opinion Mining Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7714-7719},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176925},
        abstract = {The exponential rise of Internet-based platforms, including social media networks and blogs, has led to a significant increase in user-generated content such as comments, reviews, and opinions on everyday experiences. Sentiment analysis, also known as opinion mining, involves the collection and interpretation of subjective information—opinions, attitudes, and emotions—expressed in textual data. This process plays a vital role in helping businesses, governments, and individuals make informed decisions based on public sentiment[8]. Despite its importance, sentiment analysis presents several challenges that hinder the accurate interpretation and classification of sentiments, particularly in determining sentiment polarity. Utilizing techniques from natural language processing (NLP) and text mining, sentiment analysis seeks to extract meaningful insights from unstructured text. This paper provides a comprehensive overview of the methodologies used in sentiment analysis, explores its practical applications, and critically evaluates existing approaches by examining their respective strengths and limitations. Furthermore, it delves into the key challenges associated with sentiment analysis and outlines potential directions for future research.},
        keywords = {Sentiment Analysis, Opinion Mining, Deep Learning, Natural Language Processing},
        month = {May},
        }