Sentimental Analysis of Product Review using Machine Learning
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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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