• Nandhini S
• Divya H
• Sujithra M

In today’s world, people exchange their thoughts on online social media platforms. At times, people give reviews and opinions on different products, brands, and services. Those reviews towards specific products not only improve the product quality but also influence the purchase decisions of the consumers. Thus, analysis of product review is a widely accepted platform where consumers can be aware of their requirements. The source dataset for the project was extracted from Kaggle. Analysis of various emotions from the reviews and ratings are done and based on that plain text review which can be categorized into eight basic emotions and two sentiments. The results show how sentiment analysis helps to identify every consumer's behavior and overcome the risks to meet consumers' satisfaction. Machine learning algorithms are used to build ML models and predict the accuracy of consumers’ behavior. Preprocessing techniques are also used to process the data and visualizations such as word cloud which visually represents ‘mostly used’ words in the dataset. This model can be efficiently used by online retail stores to make decisions on the production of different commodities.

Analysis – Text review – Emotions – Sentiments – Sentiment Analysis – Natural Language Processing – Machine learning – Consumer behaviour

"Operational Marketing Strategy Analysis based on Online Reviews Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 2, page no.b405-b412, February-2022, Available :http://www.jetir.org/papers/JETIR2202145.pdf

"Operational Marketing Strategy Analysis based on Online Reviews Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 2, page no. ppb405-b412, February-2022, Available at : http://www.jetir.org/papers/JETIR2202145.pdf