Assessing Topic Modeling Approaches: A Metric-Based Review

Topic modeling is a technique used to identify and comprehend thematic structures in a collection of documents. In recent years, this field has seen numerous interdisciplinary applications, including bioinformatics, software engineering, cryptocurrency, smart-home research, and human behavior. This article discusses the challenges and limitations of current topic modeling approaches, particularly their lack of reporting essential metrics for evaluating their accuracy.
To address this issue, the authors propose using a combination of metrics, such as coherence values, document entropy, number of tokens, and average word length for each topic, to provide a more comprehensive understanding of the underlying topic modeling approach. These metrics can help determine the optimal number of topics and evaluate the accuracy of the topic modeling approaches used.
The article also discusses the importance of reporting these metrics to facilitate comparison and reproducibility across different studies. By providing a detailed analysis of the metrics used in current topic modeling approaches, this study contributes to the scientific community’s understanding of this technique and its applications in various fields.
In conclusion, topic modeling is a powerful tool for identifying and comprehending thematic structures in a collection of documents. By using a combination of essential metrics, researchers can evaluate the accuracy of current topic modeling approaches and facilitate comparison across different studies. This study highlights the importance of reporting these metrics to promote transparency and reproducibility in the field of topic modeling.