Location Prediction from Social Media Contents using Location Aware Attention LSTM Network  

作　　者：Madhur Arora Sanjay Agrawal Ravindra Patel 

机构地区：[1]Department of Computer Application,University Institute of Technology,Rajiv Gandhi Proudyogiki Vishwavidyalaya,Bhopal(M.P.)462033,India [2]Department of Computer Application,National Institute of Technical Teachers Training and Research,Bhopal(M.P.)462002,India

出　　处：《Journal of Harbin Institute of Technology(New Series)》2024年第5期68-77,共10页哈尔滨工业大学学报（英文版）

摘　　要：Location prediction in social media,a growing research field,employs machine learning to identify users' locations from their online activities.This technology,useful in targeted advertising and urban planning,relies on natural language processing to analyze social media content and understand the temporal dynamics and structures of social networks.A key application is predicting a Twitter user's location from their tweets,which can be challenging due to the short and unstructured nature of tweet text.To address this challenge,the research introduces a novel machine learning model called the location-aware attention LSTM(LAA-LSTM).This hybrid model combines a Long Short-Term Memory(LSTM) network with an attention mechanism.The LSTM is trained on a dataset of tweets,and the attention network focuses on extracting features related to latitude and longitude,which are crucial for pinpointing the location of a user's tweet.The result analysis shows approx.10% improvement in accuracy over other existing machine learning approaches.

关 键 词：TWITTER social media LOCATION machine learning attention network 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]