Framework of blog data based multi-criteria weighted points of interest graph for trip planning

Haris, Erum; Gan, Keng Hoon

doi:10.3233/idt-170321

Cited by 2 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, the nature of connection between POIs represents popularity of the route among bloggers. FSPM usage for travel pattern mining with relevant case studies has been elaborated in our earlier work [25]. Consider an example scenario, Table 1 represents the output of Section 3.2.1 as a set of five processed blog entries X’ = {x 1 ’, x 2 ’, x 3 ’, x 4 ’, x 5 ’} .…”

Section: Methodsmentioning

confidence: 99%

Spatial information extraction from travel narratives: Analysing the notion of co-occurrence indicating closeness of tourist places

Haris

Gan

Tan

2019

Journal of Information Science

Self Cite

View full text Add to dashboard Cite

Recent advancements in social media have generated a myriad of unstructured geospatial data. Travel narratives are among the richest sources of such spatial clues. They are also a reflection of writers’ interaction with places. One of the prevalent ways to model this interaction is a points of interest (POIs) graph depicting popular POIs and routes. A relevant notion is that frequent pairwise occurrences of POIs indicate their geographic proximity. This work presents an empirical interpretation of this theory and constructs spatially enriched POI graphs, a clear augmentation to popularity-based POI graphs. A triplet pattern, rule-based spatial relation extraction technique SpatRE is proposed and compared with standard relation extraction systems Ollie and Stanford OpenIE. A travel blogs data set is also contributed containing labelled spatial relations. The performance is further evaluated on SemEval 2013 benchmark data sets. Finally, spatially enriched POI graphs are qualitatively compared with TripAdvisor and Google Maps to visualise information accuracy.

show abstract

Section: Methodsmentioning

confidence: 99%

Spatial information extraction from travel narratives: Analysing the notion of co-occurrence indicating closeness of tourist places

Haris

Gan

Tan

2019

Journal of Information Science

Self Cite

View full text Add to dashboard Cite

show abstract

Extraction and Visualization of Tourist Attraction Semantics from Travel Blogs

Haris

Gan

2021

IJGI

View full text Add to dashboard Cite

Travel blogs are a significant source for modeling human travelling behavior and characterizing tourist destinations owing to the presence of rich geospatial and thematic content. However, the bulk of unstructured text requires extensive processing for an efficient transformation of data to knowledge. Existing works have studied tourist places, but results lack a coherent outline and visualization of the semantic knowledge associated with tourist attractions. Hence, this work proposes place semantics extraction based on a fusion of content analysis and natural language processing (NLP) techniques. A weighted-sum equation model is then employed to construct a points of interest graph (POI graph) that integrates extracted semantics with conventional frequency-based weighting of tourist spots and routes. The framework offers determination and visualization of massive blog text in a comprehensible manner to facilitate individuals in travel decision-making as well as tourism managers to devise effective destination planning and management strategies.

show abstract

Framework of blog data based multi-criteria weighted points of interest graph for trip planning

Cited by 2 publications

References 30 publications

Spatial information extraction from travel narratives: Analysing the notion of co-occurrence indicating closeness of tourist places

Spatial information extraction from travel narratives: Analysing the notion of co-occurrence indicating closeness of tourist places

Extraction and Visualization of Tourist Attraction Semantics from Travel Blogs

Contact Info

Product

Resources

About