How does topic modeling contribute to engagement strategies for small businesses?
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Topic modeling plays a crucial role in enhancing engagement strategies for small businesses by extracting valuable insights from textual data. By utilizing topic modeling techniques like Latent Dirichlet Allocation (LDA) , businesses can identify key themes such as competitiveness, entrepreneurship networks, and industry conditions . This enables small enterprises to understand internal and external competitive factors, aiding in the development of effective engagement strategies. Moreover, non-Bayesian approaches like Additive Regularization of Topic Models (ARTM) provide a multiobjective optimization framework for topic modeling, allowing for the creation of tailored models with desired properties for engagement strategies . Through topic modeling, small businesses can leverage quantitative research methodologies to refine their understanding of market dynamics and customer preferences, ultimately leading to more targeted and successful engagement strategies.
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22 Citations | Not addressed in the paper. |
28 Jun 2022 | Topic modeling aids in identifying competitiveness factors for small businesses, enabling tailored engagement strategies based on internal and external competitive insights, enhancing business sustainability and competitiveness. |
01 Sep 2018 3 Citations | Topic modeling enhances engagement strategies for small businesses by organizing text data into clusters, identifying topics, and refining connections using a 2D vector space model with Apriori algorithm and NLP. |
| Not addressed in the paper. |
Related Questions
How to make topic modelling properly?5 answersTo properly conduct topic modeling, it is essential to consider various aspects highlighted in the research papers. Firstly, different applications may require specific topic models and inference algorithms to enhance performance. Secondly, incorporating prior knowledge and word relations in the model can improve topic coherence and human-judged topic quality. Additionally, utilizing a heuristic approach based on the rate of perplexity change can aid in determining the optimal number of topics in a model, reducing the need for extensive sensitivity studies. Moreover, relating topic modeling to community detection in complex networks can offer a more principled framework, automatically detecting the number of topics and hierarchically clustering words and documents. By integrating these insights, researchers can enhance the effectiveness and efficiency of topic modeling processes.
What are the different topic modelling techniques?5 answersTopic modelling techniques mentioned in the abstracts include Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), Top2Vec, and BERTopic. LDA is a traditional technique frequently used for topic modelling, while NMF is another traditional technique. Top2Vec and BERTopic are recent approaches that use unsupervised neural networks to develop distributed representations of texts and words. These techniques are used to identify latent themes and topics in unstructured text data. LDA and BERTopic are particularly popular in topic modelling. Other topic modelling techniques mentioned in the abstracts include Correlated Topic Model (CTM), Hierarchical Dirichlet Process (HDP), Dirichlet Multinomial Regression (DMR), and Hierarchical Pachinko Allocation (HPA).
What are the different approaches to topic modelling?5 answersTopic modelling approaches include Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), Top2Vec, and BERTopic. LDA and NMF are traditional techniques commonly used for topic modelling. Top2Vec and BERTopic are recent approaches that utilize unsupervised neural networks to develop distributed representations of texts and words. Kernel Principal Component Analysis (KernelPCA) and K-means Clustering have also been used in the BERTopic architecture for topic modelling. LDA and BERTopic are two popular topic modelling techniques, with LDA using a probabilistic approach and BERTopic using transformers and class-based TF-IDF to create dense clusters. In the context of philanthropic corporate social responsibility (PCSR) activities, LDA approaches such as Variational Bayes Inference, Gibbs Sampling, and Expectation Maximization have been considered and compared, with Expectation Maximization identified as the best technique for clustering PCSR documents.
What are the potential applications of topic modeling on social media?4 answersTopic modeling on social media has several potential applications. It can be used for market research by analyzing social media posts to understand consumer opinions and experiences related to products or services. Topic modeling can also be used to analyze information and comments related to specific events or attractions, such as the Mandalika Circuit, to gain insights and improve the quality of services. Additionally, topic modeling can help in understanding trending topics and analyzing sentiment on social media platforms. It can also be used to model communities or groups of users in social media, providing information support for decision-making at different levels of government. Overall, topic modeling on social media offers opportunities for understanding consumer behavior, improving services, and supporting decision-making processes.
How topic modeling is useful in education?5 answersTopic modeling is useful in education for various purposes. It can be used to analyze the trends and topics in educational texts, such as school textbooks. Topic modeling can also help identify the research interests and concerns of the academic community in a specific field, such as computers in education. Additionally, topic modeling can be applied to social media data, like Twitter posts, to detect and understand emerging topics and challenges in education, such as the issues faced during distance education due to the COVID-19 pandemic. By using topic modeling, researchers and educators can gain insights into the content and structure of educational materials, track the evolution of research topics, and identify areas of focus for future studies and policy development.
What is topic modelling and its application?5 answersTopic modelling is a method in text mining that quantitatively identifies the underlying semantic structure of a collection of documents. It involves detecting word and phrase patterns within the documents and automatically clustering similar expressions to characterize the set of documents. Topic modelling has various applications, including mapping innovation in companies, assessing economic sectors, and classifying firms based on topics or sectors. It is also used in analyzing scholarly articles, understanding competitive factors in small businesses, and guiding literature reviews in specific research areas. Topic modelling techniques such as Latent Semantic Analysis and Latent Dirichlet Allocation are commonly used for this purpose. Overall, topic modelling provides a valuable tool for analyzing and understanding large collections of documents, enabling researchers to uncover hidden patterns and gain insights from textual data.