Hybrid Text Embedding and Evolutionary Algorithm Approach for Topic Clustering in Online Discussion Forums
Leveraging discussion forums as a medium for information exchange has led to a surge in data, making topic clustering in these platforms essential for understanding user interests, preferences, and concerns. This study introduces an innovative methodology for topic clustering by combining text embed...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad de Salamanca (USAL) |
| Repositorio: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/162534 |
| Acceso en línea: | http://hdl.handle.net/10366/162534 |
| Access Level: | acceso abierto |
| Palabra clave: | LDA BERT K-Means Genetic Algorithms Forum Analysis |
| Sumario: | Leveraging discussion forums as a medium for information exchange has led to a surge in data, making topic clustering in these platforms essential for understanding user interests, preferences, and concerns. This study introduces an innovative methodology for topic clustering by combining text embedding techniques—Latent Dirichlet Allocation (LDA) and BERT—trained on a singular autoencoder. Additionally, it proposes an amalgamation of K-Means and Genetic Algorithms for clustering topics within triadic discussion forum threads. The proposed technique begins with a preprocessing stage to clean and tokenize textual data, which is then transformed into a vector representation using the hybrid text embedding method. Subsequently, the K-Means algorithm clusters these vectorized data points, and Genetic Algorithms optimize the parameters of the K-Means clustering. We assess the efficacy of our approach by computing cosine similarities between topics and comparing performance against coherence and graph visualization. The results confirm that the hybrid text embedding methodology, coupled with evolutionary algorithms, enhances the quality of topic clustering across various discussion forum themes. This investigation contributes significantly to the development of effective methods for clustering discussion forums, with potential applications in diverse domains, including social media analysis, online education, and customer response analysis. |
|---|