[공지사항]을 빙자한 안부와 근황 
Show more

Daily Arxiv

This is a page that curates AI-related papers published worldwide.
All content here is summarized using Google Gemini and operated on a non-profit basis.
Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.

TopicImpact: Improving Customer Feedback Analysis with Opinion Units for Topic Modeling and Star-Rating Prediction

Created by
  • Haebom

Author

Emil H aglund, Johanna Bj orklund

Outline

This paper presents a method to improve the process of extracting insights from customer reviews. It reconfigures the existing topic modeling pipeline to process individual opinion units, which contain relevant text excerpts and associated sentiment scores. By applying topic modeling to these opinion units, which are reliably extracted using large-scale language models, we generate consistent and interpretable topics and capture the sentiment associated with each topic. By correlating topics and sentiments with business metrics such as star ratings, we can gain insight into the impact of specific customer concerns on business outcomes. We present the system implementation, use cases, and their advantages over other topic modeling and classification solutions, and evaluate the effectiveness of integrating topic and sentiment modalities for consistent topic generation and accurate star rating prediction.

Takeaways, Limitations

•
Takeaways:
◦
By leveraging large-scale language models, we can effectively extract opinion units from customer reviews and apply them to topic modeling to obtain more consistent and interpretable results.
◦
You can correlate topics and sentiments with business metrics to analyze how customer concerns impact business outcomes.
◦
Provides improved performance over existing topic modeling and classification solutions.
◦
We present a method to integrate topics and sentiment modalities for accurate star rating prediction.
•
Limitations:
◦
Additional research is needed on the generalizability of the method presented in this paper (e.g., dependence on specific datasets or domains).
◦
Consideration must be given to the computational cost and resource consumption resulting from the use of large-scale language models.
◦
Further performance evaluation on different types of customer review data is needed.
👍
Made with Slashpage