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- W4384134893 abstract "The ever expanding disasters based tourism spots industry is drawing attention of folks through social networking sites, as people are travelling to corners of the world and encouraging many to fulfill their dreams. Our work is based upon retrieving the people experiences shared on twitter’s hash tag and automatically generating the rank of disasters related tourist spots in search which are safe, easily approachable and economical as per the tourist reviews. In this paper, our work is divided into data collection and filtration and then using deep learning approaches, LSTM such as word2vec, tweet2vec, hash tag2vec and NCA for feature extraction and Long Short Term Memory techniques for classification we have applied to generate results. (1) Collections of disasters based tourist spot related hashtags datasets in english and non-english language both. After that, manually classify the datasets between English and non-english languages. (2) Hash2vec has been used for making labeled datasets called Semantic-tagged tokens and (3) Apply Long Short Term Memory approach with hash2vec model have been used for representing the document and predict the rank of disasters based tourism spots hash tag. This tool will help people and travel companies to classify tourist places as per their needs and it will also help to retrieve the world’s top cities chart depending on the online ratings and reviews.." @default.
- W4384134893 created "2023-07-14" @default.
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- W4384134893 date "2023-01-01" @default.
- W4384134893 modified "2023-09-27" @default.
- W4384134893 title "Hashtag2vec: Ranking of Disaster Related Tourism Spot’s Hashtag on Twitter Data" @default.
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- W4384134893 doi "https://doi.org/10.1007/978-3-031-35641-4_8" @default.
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