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@article{181398,
        author = {G.JAYAGOPI and C.B.SUMATHI},
        title = {Optimized Sentiment Analysis of Twitter Reviews Using GOSM and Deep Sentiment Metrics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {3895-3899},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181398},
        abstract = {Sentiment analysis involves identifying and classifying opinions or sentiments expressed within textual data. Social media platforms, especially Twitter, serve as significant sources of sentiment-rich content through tweets, status updates, and blog posts. Extracting meaningful insights from such data can help understand public opinion trends. However, sentiment analysis on Twitter presents unique challenges due to informal language, frequent use of slang, misspellings, and the platform’s 140-character limit. Two widely adopted approaches in sentiment analysis are the knowledge-based and machine learning methods. This study focuses on analysing Twitter posts related to electronic products such as mobile phones and laptops using a machine learning approach. Specifically, it utilizes the Grid Optimized Search Machine (GOSM) algorithm to assess sentiments within tweets. To enhance the evaluation, a deep sentiment difference metric is introduced to measure the impact of reviews more effectively. The proposed system demonstrates promising performance, achieving an accuracy of 85%.},
        keywords = {Sentiment Analysis, Machine Learning, Opinion Mining, Neural Networks, Optimization Algorithms.},
        month = {June},
        }