Mining graphs from travel blogs: a review in the context of tour planning

Haris, Erum; Gan, Keng Hoon

doi:10.1007/s40558-017-0095-2

Cited by 9 publications

(6 citation statements)

References 53 publications

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“…A brief review of two mainstream domains, POI graph construction and spatial information extraction from travel narratives, is presented here. A comprehensive survey [11] can be referred for details.…”

Section: Related Workmentioning

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

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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.

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Section: Related Workmentioning

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

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“…"Suggestion" can be defined in two ways. First, a generic definition [11,12] is that "a sentence made by a person, usually as a suggestion or an action guide and/or conduct relayed in a particular context." Second, an application-specific definition defines suggestion as "sentences where the commenter wishes for a change in an existing product or service" [15].…”

Section: Related Workmentioning

confidence: 99%

“…In addition, consumers can benefit from these suggestions by using them to decide whether to buy a particular product or service. Such increased opinionated text has constituted the major dataset in the majority of recent research [9][10][11]. Some studies have focused on product reviews [4,5,12] related to tourism (e.g., hotel service) [10,11] and on social media data (e.g., Twitter) [13].…”

Section: Introductionmentioning

confidence: 99%

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Suggestion Mining from Opinionated Text of Big Social Media Data

Alotaibi¹,

Malik²,

Khan³

et al. 2021

Computers, Materials &Amp; Continua

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Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and services. The increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making process. To overcome this challenge, extracting suggestions from opinionated text is a possible solution. In this study, the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers' reviews. A classification using a word-embedding approach is used via the XGBoost classifier. The two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews. F1, precision, recall, and accuracy scores are calculated. The results demonstrated that the XGBoost classifier outperforms-with an accuracy of more than 80%. Moreover, the results revealed that suggestion keywords and phrases are the predominant features for suggestion extraction. Thus, this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.

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“…(Arulanandam et al, 2014;. By promptly responding to post-disaster rescue entreaties and resource requisitions (Resch et al, 2018), unraveling the nexus between crisis response and masses-social media interplay (Kersten et al, 2019), capturing the nuanced experiential tapestry of travel destinations (Haris & Gan, 2017), and delving into the intricacies of urban emotion dynamics (Resch et al, 2014), a mosaic of insights emerges.…”

Section: Introductionmentioning

confidence: 99%

Practices of education for sustainability in Renmin University of China

Liu¹

2017

Education and Sustainability

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Auto-evaluation aims to automatically evaluate a trained model on any test dataset without human annotations. Most existing methods utilize global statistics of features extracted by the model as the representation of a dataset. This ignores the influence of the classification head and loses category-wise confusion information of the model. However, ratios of instances assigned to different categories together with their confidence scores reflect how many instances in which categories are difficult for the model to classify, which contain significant indicators for both overall and category-wise performances. In this paper, we propose a Confidence-based Category Relation-aware Regression (C 2 R 2 ) method. C 2 R 2 divides all instances in a meta-set into different categories according to their confidence scores and extracts the global representation from them. For each category, C 2 R 2 encodes its local confusion relations to other categories into a local representation. The overall and category-wise performances are regressed from global and local representations, respectively. Extensive experiments show the effectiveness of our method.

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Mining graphs from travel blogs: a review in the context of tour planning

Cited by 9 publications

References 53 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

Suggestion Mining from Opinionated Text of Big Social Media Data

Practices of education for sustainability in Renmin University of China

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