Qualitative data analysis of disaster risk reduction suggestions assisted by topic modeling and word2vec

Gorro, Ken; Ancheta, Jeffrey Rosario; Capao, Kris; Oco, Nathaniel; Roxas, Rachel Edita; Sabellano, Mary Jane G.; Nonnecke, Brandie; Mohanty, Shrestha; Crittenden, Camille; Goldberg, Ken

doi:10.1109/ialp.2017.8300601

Cited by 19 publications

(4 citation statements)

References 10 publications

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“…Similarly, in the works of [12] and [13], the authors performed NLP-based techniques like topic modeling and sentiment analysis on typhoons and earthquake-related tweets in the Philippines. Another NLP-based paper [14], utilizes 976 suggestions on how their village can help them better prepare for a disaster implemented computational methods, specifically topic modeling and word2vec, to assist in the analysis of qualitative data on disaster risk reduction suggestions. Based on the results, computational methods improved the efficiency and accuracy of the data analysis process.…”

Section: Thematic Analysis Using Natural Language Processing Techniquesmentioning

confidence: 99%

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Discovering Insights via Hybrid Thematic Analysis: A Case Study on Disaster Risk Reduction and Management for Legazpi City, Albay

Abisado,

Maceda,

Rodriguez

et al. 2023

Machine Learning and Artificial Intelligence

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Understanding the perceptions and experiences of a community regarding disasters is crucial in effectively planning and implementing disaster strategies. There are two known approaches to analyzing perceptions, qualitative and automated approaches for thematic analysis. This paper aims to investigate the strengths and limitations of the mentioned approaches. Thus, using both approaches, this study analyzed data from a focus group discussion about disaster risk reduction and management and climate change adaptation conducted in a typhoon-prone city in Legazpi City, Philippines. An inductive-deductive approach for the qualitative method while a language model-assisted approach for the automatic method of extracting prominent themes from the collected responses. The results show (dis) similarities regarding themes obtained from the two approaches, specifically the emphasis on concerns about the proper distribution of relief goods and donations, proper early monitoring of potentially powerful typhoons, and other forms of threat, including politically motivated ones. From these findings, we conclude the importance of incorporating a combined manual and automatic approach for thematic analysis of natural language.

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Section: Thematic Analysis Using Natural Language Processing Techniquesmentioning

confidence: 99%

“…These topics are in the context of general disaster terms, concerns on quarrying, preparation and monitoring, concerns on donations, and local politics. Similar to related work [14], these themes were manually labeled based on the context provided by their supporting topic keywords as seen in the second column of Table 2.…”

Section: Themes From Automatic Analysismentioning

confidence: 99%

Discovering Insights via Hybrid Thematic Analysis: A Case Study on Disaster Risk Reduction and Management for Legazpi City, Albay

Abisado,

Maceda,

Rodriguez

et al. 2023

Machine Learning and Artificial Intelligence

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“…We used the BTM to extract topics in microblogs. This model has been widely used in disaster-related microblog topic extraction [56][57][58][59], as it outperforms the conventional topic models such as LDA [60]. The BTM model learns the topics through modeling the word co-occurrence patterns in the whole corpus and cluster microblogs based on the frequency and distribution of the bi-terms in the sentences.…”

Section: Typhoon-related Microblogs and Topic Analysismentioning

confidence: 99%

Understanding Human Activities in Response to Typhoon Hato from Multi-Source Geospatial Big Data: A Case Study in Guangdong, China

Huang

et al. 2022

Remote Sensing

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Every year typhoons severely disrupt the normal rhythms of human activities and pose serious threats to China’s coast. Previous studies have shown that the impact extent and degree of a typhoon can be inferred from various geolocation datasets. However, it remains a challenge to unravel how dwellers respond to a typhoon disaster and what they concern most in the places with significant human activity changes. In this study, we integrated the geotagged microblogs with the Tencent’s location request data to advance our understanding of dweller’s collective response to typhoon Hato and the changes in their concerns over the typhoon process. Our results show that Hato induces both negative and positive anomalies in humans’ location request activities and such anomalies could be utilized to characterize the impacts of wind and rainfall brought by Hato to our study area, respectively. Topic analysis of Hato-related geotagged microblogs reveals that the negative location request anomalies are closely related to damage-related topics, whereas the positive anomalies to traffic-related topics. The negative anomalies are significantly correlated with economic loss and population affected at city level as suggested by an over 0.7 adjusted R2. The changes in the anomalies can be used to portray the response and recovery processes of the cities impacted.

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“…They determine that FastText-Skip Gram model produces the best results. The authors of [10] analyse the quality of biterm topic modeling (BTM) and the word embedding approaches in the Gensim library, in a set of suggestions about disaster risk reduction strategies, provided by residents in disaster-prone areas of the Philippines. A word intrusion test was conducted, and BTM gives a strong cohesion of the words with their topics.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison and Evaluation of Different Methods for the Feature Extraction from Educational Contents

et al. 2020

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This paper analyses the capabilities of different techniques to build a semantic representation of educational digital resources. Educational digital resources are modeled using the Learning Object Metadata (LOM) standard, and these semantic representations can be obtained from different LOM fields, like the title, description, among others, in order to extract the features/characteristics from the digital resources. The feature extraction methods used in this paper are the Best Matching 25 (BM25), the Latent Semantic Analysis (LSA), Doc2Vec, and the Latent Dirichlet allocation (LDA). The utilization of the features/descriptors generated by them are tested in three types of educational digital resources (scientific publications, learning objects, patents), a paraphrase corpus and two use cases: in an information retrieval context and in an educational recommendation system. For this analysis are used unsupervised metrics to determine the feature quality proposed by each one, which are two similarity functions and the entropy. In addition, the paper presents tests of the techniques for the classification of paraphrases. The experiments show that according to the type of content and metric, the performance of the feature extraction methods is very different; in some cases are better than the others, and in other cases is the inverse.

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Qualitative data analysis of disaster risk reduction suggestions assisted by topic modeling and word2vec

Cited by 19 publications

References 10 publications

Discovering Insights via Hybrid Thematic Analysis: A Case Study on Disaster Risk Reduction and Management for Legazpi City, Albay

Discovering Insights via Hybrid Thematic Analysis: A Case Study on Disaster Risk Reduction and Management for Legazpi City, Albay

Understanding Human Activities in Response to Typhoon Hato from Multi-Source Geospatial Big Data: A Case Study in Guangdong, China

Comparison and Evaluation of Different Methods for the Feature Extraction from Educational Contents

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