The AI-Driven Sentiment Classification via Combinatorial Techniques and Reasoning

Authors

  • Devi Vaishnavi M B.E (AIML), Department of Artificial Intelligence and Machine Learning, Rajalakshmi Institute of Technology, Chennai, India
  • Merlin Christo Franklin Assistant Professor, Department of Artificial Intelligence and Machine Learning, Rajalakshmi Institute of Technology, Chennai, India
  • Tarun V Engineering Student, Department of Artificial Intelligence and Machine Learning, Rajalakshmi Institute of Technology, Chennai, India
  • Divyabharathi L Engineering Student, Department of Artificial Intelligence and Machine Learning, Rajalakshmi Institute of Technology, Chennai, India
  • Sri Nithi Murali Engineering Student, Department of Artificial Intelligence and Machine Learning, Rajalakshmi Institute of Technology, Chennai, India

DOI:

https://doi.org/10.59461/ijitra.v3i4.121

Keywords:

Sentiment analysis, Permutation, AI Logics, Fuzzy Logic

Abstract

This paper proposes an AI-driven approach to sentiment analysis, leveraging mathematical concepts such as permutations, along with logical reasoning techniques. The method involves splitting the text; permutations are used to extract n-grams. AI-driven logic is then applied for feature scoring. Finally, fuzzy logic integrates these scores to classify sentiments. This approach focuses on enhancing sentiment classification accuracy by blending AI-based features.

Author Biographies

Devi Vaishnavi M, B.E (AIML), Department of Artificial Intelligence and Machine Learning, Rajalakshmi Institute of Technology, Chennai, India

Passionate student currently pursuing a Bachelor’s degree in Artificial Intelligence and Machine Learning at the Rajalakshmi Institute of Technology in Chennai.  Email:devivaishnavi.m.2023.aiml@ritchennai.edu.in 

Merlin Christo Franklin, Assistant Professor, Department of Artificial Intelligence and Machine Learning, Rajalakshmi Institute of Technology, Chennai, India

Enthusiastic Assistant Professor with almost 3 years of expertise in Engineering, with a passion for teaching and guiding students towards academic excellence. Proficient in software development, data analysis, and database management. distinguished with the title of "Best Educator" at DMI College of Engineering.  My research has been recognized at several prestigious forums, including a groundbreaking paper on the "Key Aggregate Cryptosystem for Scalable Data Sharing in Cloud Storage," which secured 2nd place at the 6th National Level Technical Symposium FALCON’16. Earned a Merit in the Business English Certificate Preliminary Examination from the University of Cambridge ESOL Examinations.She can be contacted at email: merlinchristo.f@ritchennai.edu.in

Tarun V, Engineering Student, Department of Artificial Intelligence and Machine Learning, Rajalakshmi Institute of Technology, Chennai, India

Passionate student currently pursuing a Bachelor’s degree in Artificial Intelligence and Machine Learning at the Rajalakshmi Institute of Technology in Chennai. Email: tarun.v.2023.aiml@ritchennai.edu.in 

Divyabharathi L, Engineering Student, Department of Artificial Intelligence and Machine Learning, Rajalakshmi Institute of Technology, Chennai, India

Passionate student currently pursuing a Bachelor’s degree in Artificial Intelligence and Machine Learning at the Rajalakshmi Institute of Technology in Chennai. Email : divyabharathi.l..2023.aiml@ritchennai.edu.in 

Sri Nithi Murali, Engineering Student, Department of Artificial Intelligence and Machine Learning, Rajalakshmi Institute of Technology, Chennai, India

Passionate student currently pursuing a Bachelor’s degree in Artificial Intelligence and Machine Learning at the Rajalakshmi Institute of Technology in Chennai. Email: srinithimurali.2023.aiml@ritchennai.edu.in 

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Published

2024-12-26

How to Cite

Maddali, D. V., Franklin, M. C., V, T., L, D., & Murali, S. N. (2024). The AI-Driven Sentiment Classification via Combinatorial Techniques and Reasoning. International Journal of Information Technology, Research and Applications, 3(4), 50–56. https://doi.org/10.59461/ijitra.v3i4.121

Issue

Section

Regular Issue