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...

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Vydané v:Advances in distributed computing and artificial intelligence journal Ročník 13; s. e31448
Hlavní autori: Bouabdallaoui, Ibrahim, Guerouate, Fatima, Sbihi, Mohammed
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Salamanca Ediciones Universidad de Salamanca 01.01.2024
Predmet:
bert
Cluster analysis
Clustering
Data exchange
Data points
Effectiveness
Embedding
Evolutionary algorithms
forum analysis
Genetic algorithms
k-means
lda
Vector quantization
ISSN:2255-2863, 2255-2863
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2255-2863
2255-2863
DOI:10.14201/adcaij.31448