# Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments
###### Abstract  
The tourism industry has experienced fast and sustainable growth over the years in the economic sector. The data available online on the ever-growing tourism sector must be given importance as it provides crucial economic insights, which can be helpful for consumers and governments. Natural language processing (NLP) techniques have traditionally been used to tackle the issues of structuring of unprocessed data, and the representation of the data in a knowledge-based system. NLP is able to capture the full richness of the text by extracting the entity and relationship from the processed data, which is gathered from various social media platforms, webpages, blogs, and other online sources, while successfully taking into consideration the semantics of the text. With the purpose of detecting connections between tourism and economy, the research aims to present a visual representation of the refined data using knowledge graphs. In this research, the data has been gathered from Twitter using keyword extraction techniques with an emphasis on tourism and economy. The research uses TextBlob to convert the tweets to numeric vector representations and further uses clustering techniques to group similar entities. A cluster-wise knowledge graph has been constructed, which comprises a large number of relationships among various factors, that visualize entities and their relationships connecting tourism and economy.  
t ouptas 202212128105  
documented on online resources. In the research, the keyword method was used to extract the unfiltered data, which assisted in discovering the relationship linkage between tourism and economy. The methodology used in this research takes a closer look into the use of knowledge graphs in order to find relevant and substantial relationships.  
Knowledge graphs have evolved as an appealing concept for organizing the world's data and ideas, which are used in semantic analysis of data that is collected from diverse sources. Knowledge graphs help to understand the context of the subject, which gives more information about the topic under study, which in turn allows for a more thorough examination and understanding of the discovered interpretation and relationship. Knowledge graphs are becoming critical in expressing knowledge retrieved through natural language processing and machine learning techniques. Knowledge graphs that are inputted in advanced and evolved machine learning models help to make more accurate and precise predictions [7,8].  
Clustering algorithms are also a vital part of establishing the key relationship linkage between tourism and economy. Clustering is fundamentally a type of unsupervised learning method that pulls references from datasets, which include input data with no labeled responses. It is often used as a procedure to discover relevant structure, explanatory underlying processes, generative features, and groups in a given dataset [9]. Clustering is the process of separating a population or set of data points into groups such that data points in the same group are more similar to other data points in the same group and different to data points in other groups. It is essentially a collection of items based on their similarity and dissimilarity.  
The topics of natural language processing, clustering, entity relationship extraction, and knowledge graphs are introduced in Section 2--Related Work, where previously conducted studies are discussed. Section 3--Methodology discusses the process used to specifically find pertinent relationships between tourism and economy. The method is broken down into various subsections, data collection, data preprocessing, vectorization, sentiment labeling, clustering, selecting the best cluster, cluster wise entity relationship extraction, and knowledge representation. The following, Section 4--Experimental Result, shows the outcome of the study, and finally Section 5--Conclusion and Future Scope talks about the impact of the research and thoughts on the future of this dynamically evolving area of study.