# Marketing Insights from Reviews Using Topic Modeling with BERTopic and Deep Clustering Network
## 1 Introduction  
In the digital age, consumers have increasingly been leaving and sharing reviews about various products and services through online platforms. These review data have become a valuable asset for companies as the data can be used to understand consumer opinions and preferences, thereby improving marketing strategies. However, effectively analyzing a large volume of review data and extracting useful information from it is a challenging task.  
This research proposes a method of extracting and analyzing marketing-related information based on review data using clustering-based topic modeling as the core technique. A clustering algorithm is a computational method used in data analysis and machine learning to group a set of data points into clusters based on their similarities. Topic modeling is a natural language processing technique used to extract hidden topics from text data. Text data can contain various topics, and each document may be related to multiple topics. Topic modeling automatically identifies these topics and estimates which topics each document is composed of.  
Topic modeling and clustering algorithms are a methodology widely used in marketing, such as for customer segmentation and recommendation systems, as they group data with similar characteristics. In a study [1], product description data were collected from an e-commerce platform that sells mobile phones, and topic modeling was conducted using LDA, a traditional topic modeling method. The study then extracted the concept of products and the relationships between each feature using keywords extracted from each topic, constructing a knowledge graph to find semantic knowledge about the products. LDA has also been utilized for topic modeling of consumer-related topics in various domains, such as food distribution, food tourism, apartment interior design, and airline services in Korea, to analyze consumer opinions [2; 3; 4; 5].  
In this study, we introduce a process that utilizes clustering algorithms based on review data that are accessible from external sources to analyze consumer intent and extract the relationship between products. By clustering review data left by consumers, groupswith similar consumer opinions can be formed, enabling the identification of consumer purchase intent and the relevance between products. This provides important information for companies to understand the association between specific products and other products, allowing them to develop sales strategies. Additionally, we present a process to extract the advantages and disadvantages of products from review data. Using topic modeling, we classify review data into respective topics and extract the advantages and disadvantages of products from each topic. This allows companies to identify which products consumers evaluate most positively and areas that need improvement. It can serve as valuable reference material for product development and marketing strategy formulation.  
There are a variety of topic modeling algorithms, but this study focuses on BERTopic [6]. BERTopic demonstrates a better performance as it leverages BERT [7] for vector embedding and considers the semantics and contextual information of the text. BERTopic is a topic modeling approach that utilizes BERT's embedding ability and demonstrates that the task can be addressed simply by employing a clustering-based approach. In this study, other clustering algorithms with good performance, such as the DCN, have been used as alternatives to generate results and compare their performances. By incorporating different clustering algorithms into the basic structure of BERTopic, this research aims to explore and compare the results obtained. This will provide insights into the effectiveness and performance of various clustering algorithms in the context of topic modeling.