Analyzing Message Board Content: Latent Dirichlet Allocation Vs Stack Ensemble Techniques
1Ugorji C. Calistus, 2Chika R. Okonkwo, 3Chika I. Obi-Okonkwo and 4Obikwelu R. Okonkwo
1,2,4 Department of Computer Science, Nnamdi Azikiwe University, Awka
3ICT Department, Federal Radio Corporation of Nigeria (FRCN), Enugu.
Email: Ugochuks2@gmail.com; chikaokon@yahoo.com, cobiokonkwo@yahoo.com and ro.okonkwo@unizik.edu.ng
ABSTRACT
Message boards and online forums have become ubiquitous platforms for users to express opinions, seek information, and engage in discussions across a wide range of topics. Analyzing the vast amount of content generated on these platforms presents a significant challenge, particularly in extracting meaningful insights and identifying underlying themes or topics. In this paper, we compare two prominent methodologies for analyzing message board content: Latent Dirichlet Allocation (LDA) and Stack Ensemble Techniques.Latent Dirichlet Allocation (LDA) is a generative probabilistic model commonly used for topic modeling. It aims to discover latent topics within a corpus by assigning probabilities to words belonging to each topic and documents being a mixture of topics. LDA has been widely employed in various natural language processing tasks, including sentiment analysis, document classification, and information retrieval. We delve into the application of LDA specifically in the context of message board content analysis, discussing its strengths, limitations, and practical considerations.Stack Ensemble Techniques, on the other hand, represent a more recent approach that leverages the power of ensemble learning in the context of text data analysis. Ensemble methods combine multiple models to improve predictive performance or provide more robust results compared to individual models. Stack ensemble techniques involve training multiple base models and then combining their predictions using a meta-learner, often resulting in superior performance compared to standalone models. We explore the potential of stack ensemble techniques in analyzing message board content and highlight their advantages over traditional methods like LDA.Furthermore, we conduct a comparative analysis between LDA and stack ensemble techniques in the specific task of message board content analysis. This includes evaluating their effectiveness in identifying and categorizing topics, handling noisy or ambiguous text data, scalability to large datasets, and interpretability of results. We discuss empirical findings and provide insights into when each approach may be more suitable based on the characteristics of the dataset and the objectives of the analysis.Through this comprehensive exploration, we aim to contribute to the understanding of methodologies for analyzing message board content and provide guidance to researchers and practitioners in selecting appropriate techniques based on their specific requirements and constraints. Our analysis sheds light on the strengths and weaknesses of both LDA and stack ensemble techniques, paving the way for further advancements in the field of text data analysis and topic modeling in online communities.
KEYWORDS: Document Clustering, Bayesian Inference., Message boards, Online forums, Text data analysis, Latent Dirichlet Allocation (LDA), Stack Ensemble Techniques, Topic modeling, Online community dynamics.
CITE AS: Ugorji C. Calistus, Chika R. Okonkwo, Chika I. Obi-Okonkwo and Obikwelu R. Okonkwo (2024). Analyzing Message Board Content: Latent Dirichlet Allocation Vs Stack Ensemble Techniques. RESEARCH INVENTION JOURNAL OF ENGINEERING AND PHYSICAL SCIENCES 3(2):18-26.