Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis

By Byeongki Jeong, Janghyeok Yoon, Jae-Min Lee. International Journal of Information Management, 2017

308 citations recorded by the source corpus.

Abstract Social media data have recently attracted considerable attention as an emerging voice of the customer as it has rapidly become a channel for exchanging and storing customer-generated, large-scale, and unregulated voices about products. Although product planning studies using social media data have used systematic methods for product planning, their methods have limitations, such as the difficulty of identifying latent product features due to the use of only term-level analysis and insufficient consideration of opportunity potential analysis of the identified features. Therefore, an opportunity mining approach is proposed in this study to identify product opportunities based on topic modeling and sentiment analysis of social media data. For a multifunctional product, this approach can identify latent product topics discussed by product customers in social media using topic modeling, thereby quantifying the importance of each product topic. Next, the satisfaction level of each product topic is evaluated using sentiment analysis. Finally, the opportunity value and improvement direction of each product topic from a customer-centered view are identified by an opportunity algorithm based on product topics’ importance and satisfaction. We expect that our approach for product planning will contribute to the systematic identification of product opportunities from large-scale customer-generated social media data and will be used as a real-time monitoring tool for changing customer needs analysis in rapidly evolving product environments.

Subfields: Artificial Intelligence, Organizational Behavior and Human Resource Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/18edb0d4465da7d91673a2337762ae0a724c2b18

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nhan Cach Dang
2. Fernando De la Prieta Pintado
3. María N. Moreno García

In Organizational Behavior and Human Resource Management

1. Nhan Cach Dang
2. Fernando De la Prieta Pintado
3. María N. Moreno García

In Sociology and Political Science

1. Nhan Cach Dang
2. Fernando De la Prieta Pintado
3. María N. Moreno García

Citations in the corpus

• Sentiment Analysis Based on Deep Learning: A Comparative Study (2020, methodology): “[48] A product opportunity mining approach based on topic modeling and sentiment analysis LDA-based topic modeling, sentiment analysis, and opportunity algorithm Twitter, Facebook, Instagram, and Reddit Identification of product development opportunities from customer-generated social media data”