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.
Abstract-The Roll cage is the most essential part of an ATV. It is like a 3-dimensional protection provided to the driver which is very crucial in determining the shape of the overall vehicle. It is the roll cage which bears the weight of all the systems like power terrain, suspension, steering and braking. The roll cage designed here is in accordance with SAE BAJA 2018 rulebook. Now SAE stands for society of automotive engineers and BAJA is their official competition where Engineering undergraduates try to design and build an ATV which is also market ready.
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