Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features
Knowing the location of a social media user and their posts is important for various purposes, such as the recommendation of location-based items/services, and locality detection of crisis/disasters. This paper describes our submission to the shared task {``}Geolocation Prediction in Twitter{''} of the 2nd Workshop on Noisy User-generated Text. In this shared task, we propose an algorithm to predict the location of Twitter users and tweets using a multinomial Naive Bayes classifier trained on Location Indicative Words and various textual features (such as city/country names, {\#}hashtags and @mentions). We compared our approach against various baselines based on Location Indicative Words, city/country names, {\#}hashtags and @mentions as individual feature sets, and experimental results show that our approach outperforms these baselines in terms of classification accuracy, mean and median error distance.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationSimilar Papers 제목 키워드 기반
End-to-end Network for Twitter Geolocation Prediction and Hashing
We propose an end-to-end neural network to predict the geolocation of a tweet. The network takes as input a number of raw Twitter metadata such as the tweet message and associated user account information. Our model is l…
Feature EngineeringGeolocation Prediction in Social Media Data by Finding Location Indicative Words
A Unified Neural Network Model for Geolocating Twitter Users
Locations of social media users are important to many applications such as rapid disaster response, targeted advertisement, and news recommendation. However, many users do not share their exact geographical coordinates d…
Disaster ResponseNews RecommendationTwitter Geolocation using Knowledge-Based Methods
Automatic geolocation of microblog posts from their text content is particularly difficult because many location-indicative terms are rare terms, notably entity names such as locations, people or local organisations. The…
Entity LinkingGraph EmbeddingStock Market PredictionTwitter User Geolocation Using a Unified Text and Network Prediction Model
We propose a label propagation approach to geolocation prediction based on Modified Adsorption, with two enhancements:(1) the removal of "celebrity" nodes to increase location homophily and boost tractability, and (2) he…