| Author Name | Affiliation | | Madhur Arora | Department of Computer Application, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, BhopalM.P. 462033, India | | Sanjay Agrawal | Department of Computer Application, National Institute of Technical Teachers Training and Research, Bhopal M.P. 462002, India | | Ravindra Patel | Department of Computer Application, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, BhopalM.P. 462033, India |
|
| Abstract: |
| 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. |
| Key words: Twitter social media location machine learning attention network |
| DOI：10.11916/j.issn.1005-9113.2023087 |
| Clc Number:TP391 |
| Fund: |
