An Entity-Based Fine-Grained Geolocalization of User Generated Short Text

Li, Yongjun; Ji, Wei; Deng, Yao; Gao, Xing

doi:10.1109/access.2020.3042813

Cited by 1 publication

(2 citation statements)

References 27 publications

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“…Li et al discussed the application of fine-grained geolocation based on extraction of location related entities from non-geotagged Tweet posts [20]. Iso et al deployed a convolutional mixture density network to predict Tweets' geolocation along with ambiguity in prediction based on a probability distribution.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we present our ongoing work to predict Twitter posts' geolocation at a granular level such as neighborhood, zipcode, and longitude with latitude values. While geolocating Twitter posts has been researched in the past [29,31,35], granular location prediction [10,20] is rare and underexplored, which is often critical for many domains. In this work, we collaborate with urban planning analysts to not only assess the success of our modeling task by an iterative modeling pipeline but also incorporate their valuable domain expertise in making modeling decisions to refine model performance (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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Fine-grained Geolocation Prediction of Tweets with Human Machine Collaboration

Dutt,

Das

2021

Preprint

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Twitter is a useful resource to analyze peoples' opinions on various topics. Often these topics are correlated or associated with locations from where these Tweet posts are made. For example, restaurant owners may need to know where their target customers eat with respect to the sentiment of the posts made related to food, policy planners may need to analyze citizens' opinion on relevant issues such as crime, safety, congestion, etc. with respect to specific parts of the city, or county or state. As promising as this is, less than 1% of the crawled Tweet posts come with geolocation tags. That makes accurate prediction of Tweet posts for the non geo-tagged tweets very critical to analyze data in various domains. In this research, we utilized millions of Twitter posts and end-users domain expertise to build a set of deep neural network models using natural language processing (NLP) techniques, that predicts the geolocation of non geo-tagged Tweet posts at various level of granularities such as neighborhood, zipcode, and longitude with latitudes. With multiple neural architecture experiments, and a collaborative human-machine workflow design, our ongoing work on geolocation detection shows promising results that empower end users to correlate relationship between variables of choice with the location information.

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Section: Related Workmentioning

confidence: 99%