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ISSN 2063-5346
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DEEP LEARNING MODEL TO RETRIEVES THE LOCATION ENTITY FROM USER TEXT POSTS

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Mehveen Mehdi Khatoon1*,Tasneem Rahath2, Ruhiat Sultana3
» doi: 10.48047/ecb/2023.12.si5a.0607

Abstract

Background/objectives: The position must be predicted before any analysis can be done. One of the analyses used to process geographic information is location analysis (GI). Methodology: The Global Positioning System (GPS) on a user's device, for instance, can be used to add latitude and longitude to posts or tweets on social media. Regrettably, not every user or content producer includes latitude and longitude in their tweets. Using the user's text posts is one method for determining where they are in the world. This method takes the user's text post and retrieves the location entity from it. The predicted location candidate is the extracted location entity. With the use of user data, we hope to include another strategy in this study. Findings: The user who tweeted the tweet then adds more details to the classification of the retrieved location entities. Later, this classification will determine if a user's post is relevant or not. For the model being utilised, deep learning would be combined.

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