Abstract
Customer reviews significantly influence consumer decisions and business strategies, requiring more advanced analytical tools to collect these valuable insights. This study examines a recent online application that analyzes customer reviews using abstractive summarization and sentiment analysis. The application allows users to monitor customer feedback through abstractive summaries and sentiment scores. The reviews can be directly pasted or uploaded via a text file for analysis. This article assesses the application across five different use cases, addressing challenges related to satisfaction, mixed reviews, recovery strategies, dissatisfaction, and sarcastic reviews. The research advocates ongoing exploration and refinement of artificial intelligence and machine learning applications, emphasizing the synergistic potential of abstractive summarization and sentiment analysis for effectively monitoring customer reviews and preferences. This practical tool empowers businesses and practitioners to make data-driven decisions based on customer feedback. Access to the application: https://mahaq.pythonanywhere.com/.
| Original language | English |
|---|---|
| Pages (from-to) | 1271-1285 |
| Number of pages | 15 |
| Journal | Journal of Marketing Analytics |
| Volume | 13 |
| Issue number | 4 |
| Early online date | 11 Feb 2025 |
| DOIs | |
| Publication status | Published - 11 Feb 2025 |
Keywords
- Abstractive summary
- Artificial intelligence
- Customer reviews
- Machine learning
- Sentiment analysis
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