Text mining techniques for Exploring Customer Sentiments towards Packaged Organic Foods in India

Abstract

Changing customer perception towards organic food is inspiring people to shift to organic counterparts of conventionally available packaged food products. They are willing to purchase the organically manufactured and packaged products in spite of a premium price tag. They thus possess some inherent expectations post purchase of organic food products. The purpose of this study is to explore customer levels of satisfaction and dissatisfaction on various attributes or features of packaged organic food products available in Indian market. For this purpose, 26,905 Online Customer Reviews of 100 unique packaged organic food products were collected and analysed using Word Clouds and Bar Graphs. Text mining techniques of Natural Language Processing (NLP) were applied to transform the unstructured textual review data into a structured form for identification of meaningful patterns and useful insights. Results confirmed that the customers were more dissatisfied than they were satisfied on product features of Price and Smell. On the other hand, Flavor, Delivery and Taste were the factors of highest customer satisfaction as compared to dissatisfaction shown towards each of these product features. Overall, Taste, Quality and Packaging saw maximum number of customer reviews as well as overall customer satisfaction post purchase of organic food products online. The study has implications for common shoppers of organic food products, manufacturers, marketers as well as retailers. The research provides a better understanding of consumer opinion after purchase and consumption of organic food products online.