Incorporating product trends into innovation processes is imperative for companies to meet customers’ expectations and to stay competitive in fiercely opposing markets. Currently, aspect-based sentiment analysis has proven an effective approach for investigating and tracking towards products and corresponding features from social media. However, existing trend analysis tools on the market that offer aspect-based sentiment analysis capabilities, do not meet the requirements regarding the use case Product Development. Therefore, based on these requirements, we implemented an artifact by following the design science research. We applied our tool to real-world social media data (37,638 Yelp reviews) from one major fast-food restaurant in the US, and thereby demonstrated that our tool is capable of identifying remarkable and fine-grained product trends.
CITATION STYLE
Wörner, J., Konadl, D., Schmid, I., & Leist, S. (2022). Supporting Product Development by a Trend Analysis Tool Applying Aspect-Based Sentiment Detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13229 LNCS, pp. 68–80). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-06516-3_6
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