Volume-5 Issue-2, February 2025 – Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) https://www.ijainn.latticescipub.com Exploring Innovation | ISSN: 2582-7626 (Online) | A Periodical Journal | Reg. No: C/1383209 | Published by Lattice Science Publication (LSP) Fri, 28 Feb 2025 04:54:42 +0000 en-US hourly 1 https://wordpress.org/?v=7.0 https://www.ijainn.latticescipub.com/wp-content/uploads/2021/01/cropped-IJAINN-logo-WF-32x32.png Volume-5 Issue-2, February 2025 – Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) https://www.ijainn.latticescipub.com 32 32 B109505020225 https://www.ijainn.latticescipub.com/portfolio-item/b109505020225/ Mon, 24 Feb 2025 09:22:10 +0000 https://www.ijainn.latticescipub.com/?post_type=portfolio&p=2319 The Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) has ISSN 2582-7626 (online), open-access, peer-reviewed, periodical bi-monthly international journal, which is published by Lattice Science Publication (LSP) in February, April, June, August, October and December.The aim of the journal is to publish high quality peer–reviewed original articles in the area of Artificial Intelligence and Neural Networking that covers Neural Networks, Fuzzy Logic, Simulated Biological Evolution Algorithms (Like Genetic Algorithm), Ant Colony Optimization, Reasoning and Evolution, Intelligence Applications, Computer Vision and Speech Understanding, Multimedia and Cognitive Informatics, Data Mining and Machine Learning Tools, Heuristic and Ai Planning Strategies and Tools, Computational Theories of Learning, Technology and Computing (Like Particle Swarm Optimization), Intelligent System Architectures, Knowledge Representation, Bioinformatics, Natural Language Processing, Multiagent Systems, Supervised Learning, Unsupervised Learning, Deep Learning, Big Data and AI Approaches, Reinforcement Learning, Learning with Generative Adversarial Networks, Regression and Prediction, Problem Solving and Planning, Clustering, Classification, Neural Information Processing, Vision and Speech Perception, Heterogeneous and Streaming Data, Probabilistic Models and Methods, Reasoning and Inference, Marketing and Social Sciences, Knowledge Discovery, Web Mining, Information Retrieval, Design and Diagnosis, Game Playing, Streaming Data, Music Modelling and Analysis, Robotics and Control, Multi-Agent Systems. #Neural Networks #Fuzzy Logic #Simulated Biological Evolution Algorithms (Like Genetic Algorithm) #Ant Colony Optimization #Reasoning and Evolution #Intelligence Applications #Computer Vision and Speech Understanding #Multimedia and Cognitive Informatics #Data Mining and Machine Learning Tools #Heuristic and AI Planning Strategies and Tools #Computational Theories of Learning #Technology and Computing (Like Particle Swarm Optimization) #Intelligent System Architectures #Knowledge Representation #Bioinformatics #Natural Language Processing #Multiagent Systems #Supervised Learning #Unsupervised Learning #Deep Learning #Big Data and AI Approaches #Reinforcement Learning #Learning with Generative Adversarial Networks #Regression and Prediction #Problem Solving and Planning #Clustering #Classification #Neural Information Processing #Vision and Speech Perception #Heterogeneous and Streaming Data #Probabilistic Models and Methods #Reasoning and Inference #Marketing and Social Sciences #Knowledge Discovery #Web Mining, Information Retrieval #Design and Diagnosis #Game Playing #Streaming Data #Music Modelling and Analysis #Robotics and Control #Multi-Agent systems #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

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]]> The Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) has ISSN 2582-7626 (online), open-access, peer-reviewed, periodical bi-monthly international journal, which is published by Lattice Science Publication (LSP) in February, April, June, August, October and December.The aim of the journal is to publish high quality peer–reviewed original articles in the area of Artificial Intelligence and Neural Networking that covers Neural Networks, Fuzzy Logic, Simulated Biological Evolution Algorithms (Like Genetic Algorithm), Ant Colony Optimization, Reasoning and Evolution, Intelligence Applications, Computer Vision and Speech Understanding, Multimedia and Cognitive Informatics, Data Mining and Machine Learning Tools, Heuristic and Ai Planning Strategies and Tools, Computational Theories of Learning, Technology and Computing (Like Particle Swarm Optimization), Intelligent System Architectures, Knowledge Representation, Bioinformatics, Natural Language Processing, Multiagent Systems, Supervised Learning, Unsupervised Learning, Deep Learning, Big Data and AI Approaches, Reinforcement Learning, Learning with Generative Adversarial Networks, Regression and Prediction, Problem Solving and Planning, Clustering, Classification, Neural Information Processing, Vision and Speech Perception, Heterogeneous and Streaming Data, Probabilistic Models and Methods, Reasoning and Inference, Marketing and Social Sciences, Knowledge Discovery, Web Mining, Information Retrieval, Design and Diagnosis, Game Playing, Streaming Data, Music Modelling and Analysis, Robotics and Control, Multi-Agent Systems. #Neural Networks #Fuzzy Logic #Simulated Biological Evolution Algorithms (Like Genetic Algorithm) #Ant Colony Optimization #Reasoning and Evolution #Intelligence Applications #Computer Vision and Speech Understanding #Multimedia and Cognitive Informatics #Data Mining and Machine Learning Tools #Heuristic and AI Planning Strategies and Tools #Computational Theories of Learning #Technology and Computing (Like Particle Swarm Optimization) #Intelligent System Architectures #Knowledge Representation #Bioinformatics #Natural Language Processing #Multiagent Systems #Supervised Learning #Unsupervised Learning #Deep Learning #Big Data and AI Approaches #Reinforcement Learning #Learning with Generative Adversarial Networks #Regression and Prediction #Problem Solving and Planning #Clustering #Classification #Neural Information Processing #Vision and Speech Perception #Heterogeneous and Streaming Data #Probabilistic Models and Methods #Reasoning and Inference #Marketing and Social Sciences #Knowledge Discovery #Web Mining, Information Retrieval #Design and Diagnosis #Game Playing #Streaming Data #Music Modelling and Analysis #Robotics and Control #Multi-Agent systems #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

Sentimental Analysis of Product Review using Machine LearningCROSSMARK Color horizontal
Swamini E Chavan1, Archana D Ambhure2, Mauli H Karche3, Tushar A Kolhe4, Vinita Kute5

1Swamini E Chavan, Bhivarabai Sawant Institute of Technology and Research (BSIOTR), Pune (Maharashtra), India.

2Archana D Ambhure, Bhivarabai Sawant Institute of Technology and Research (BSIOTR), Pune (Maharashtra), India.

3Mauli H Karche, Bhivarabai Sawant Institute of Technology and Research (BSIOTR), Pune (Maharashtra), India.

4Tushar A Kolhe, Bhivarabai Sawant Institute of Technology and Research (BSIOTR), Pune (Maharashtra), India.

5Prof. Vinita Kute, Bhivarabai Sawant Institute of Technology and Research (BSIOTR), Pune (Maharashtra), India.   

Manuscript received on 14 December 2024 | First Revised Manuscript received on 17 January 2025 | Second Revised Manuscript received on 30 January 2025 | Manuscript Accepted on 15 February 2025 | Manuscript published on 28 February 2025 | PP: 1-4 | Volume-5 Issue-2, February 2025 | Retrieval Number: 100.1/ijainn.B109505020225 | DOI: 10.54105/ijainn.B1095.05020225

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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Sentiment analysis of product reviews plays a crucial role in understanding consumer feedback, improving customer experience and making informed business decisions. This paper explores the application of machine learning and deep learning algorithms to effectively classify and analyse the sentiment of product reviews. Traditional machine learning techniques, such as Naïve Bayes, Support Vector Machines (SVM) and Random Forests are employed for sentiment classification based on manually engineered features. Simultaneously, deep learning approaches like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are leveraged to automatically learn complex representations from raw text data. The study compares the performance of these methods in terms of accuracy, precision, recall and F1-score. Additionally, pre-trained language models such as BERT are incorporated to enhance contextual understanding. Experimental results demonstrate that deep learning models particularly LSTM and BERT, outperform traditional machine learning techniques in capturing sentiments. This analysis provides valuable insights into the effectiveness of different algorithms in sentiment analysis tasks, paving the way for more advanced applications in natural language processing and customer sentiment evaluation.

Keywords: Sentimental Analysis, Deep learning, Naïve bayes, Product Review, Feature Extraction.
Scope of the Article: Neural Networks

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