Consumer Acceptance of Beer: An Automated Sentiment Analysis Approach
Selecting the correct methodology to better understand how consumers perceive food products is a challenging task for the food industry and sensory researchers alike. Free comment tasks (FC) utilize the advantages of open-ended questions to generate intuitive comments from untrained consumers to help identify and describe sensory attributes of products. However, FC data is typically analyzed using text analysis done by hand and is very cumbersome to organize and interpret. There is a growing need and interest to add to the library of data analysis tools used to understand FC data and consumer acceptance studies. Sentiment analysis is an opinion mining tool commonly used in marketing and computer science that extracts the emotional valence of the author from an unstructured text in the form of a sentiment score. A few studies in sensory evaluation use lexicon-based sentiment analysis which has many drawbacks: it is time-consuming, requires a large amount of data and dictionaries need to be tailored for food. We used a deep learning sentiment analysis approach to analyze and predict consumer sentiment/acceptance. The research objectives of this study are 1) to explore quicker and automated methods of sentiment analysis to better understand and predict consumer acceptance, and 2) to examine the advantages and disadvantages of sentiment analysis as a data analysis tool in sensory evaluation. We avoided the pitfalls of creating a sentiment lexicon by using online beer reviews to train a word embedding model where all of the relevant words in the review are converted into vectors. We used the distance and similarity (clustering) of the vectors to determine taste/flavor attributes that correspond to negative and positive sentiment. Next, to validate and test our model we gathered FC data in a consumer acceptance study. Panelists (N=68) were presented with six beers, one at a time and were instructed to taste and smell before leaving comments. We performed sentiment analysis on the FC data, and we compared our deep learning sentiment analysis model with three other pre-existing sentiment analysis models: SentimentR, VADER, and Liu and Hu opinion lexicon. Our deep learning sentiment analysis model had the highest accuracy (69%) and precision rate (73%). Overall, our findings provide an early look into the advantages and disadvantages of sentiment analysis applied to FC data in sensory evaluation.