LDA Based Topic Modeling on Hospital Facebook Posts

Kamaruddin, Siti Sakira; Ahmad, Farzana Kabir; Taiye, Mohammed Ahmed

doi:10.1007/978-3-031-00828-3_14

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first step refers to the research data. The data was obtained from Facebook similarly as in many previous studies (Ferrer-Rosell et al, 2020;Mu sanovi c et al, 2021a, b;Su et al, 2015;Kamaruddin et al, 2022). Given the large number of hotels in Italy (N 5 601) and as several steps in data collection were done manually, it was necessary to narrow and refine the sample.…”

Section: Research Methodology Data Collection and Text Screeningmentioning

confidence: 99%

Corporate communication on social media: a case study before and during pandemic COVID-19

Mušanović

Dorčić

Gregorić

2023

CCIJ

View full text Add to dashboard Cite

PurposeThe purpose of this study is to examine how hotel brands communicate on social media before and during the pandemic coronavirus disease 2019 (COVID-19) in relation to the tourism season.Design/methodology/approachTo gain insights into the communication of Italian hotel brands on social media, this study applies a qualitative methodology. Using the text mining technique, topic modelling was conducted on a sample of 5,032 posts from Italian 5-star hotel brands shared on the hotels' official Facebook pages.FindingsThe results show that hotel brands used essentially the same communication strategy in the tourism seasons before and after the pandemic outbreak, but with a particular focus on trust, safety and cordiality during the pandemic. Hotel brands focussed intensively on brand awareness, customer engagement and special activities that promote memorable and authentic experiences as well as luxury service quality.Originality/valueThis study contributes to the theoretical and empirical sense by bridging the concepts of tourism and hospitality, social media and corporate communication.

show abstract

Section: Research Methodology Data Collection and Text Screeningmentioning

confidence: 99%

Corporate communication on social media: a case study before and during pandemic COVID-19

Mušanović

Dorčić

Gregorić

2023

CCIJ

View full text Add to dashboard Cite

show abstract

A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems

Youn,

Lee,

Han

et al. 2024

Sustainability

View full text Add to dashboard Cite

The increasing density of urban populations has spurred interest in utilizing underground space. Underground logistics systems (ULS) are gaining traction due to their effective utilization of this space to enhance urban spatial efficiency. However, research on technological advancements in related fields remains limited. To address this gap, we applied a data-driven approach using patent data related to the ULS to develop a technology roadmap for the field. We employed Latent Dirichlet Allocation (LDA), a machine learning-based topic modeling technique, to categorize and identify six specific technology areas within the ULS domain. Subsequently, we conducted portfolio analytics to pinpoint technology areas with high technological value and to identify the major patent applicants in these areas. Finally, we assessed the technology market potential by mapping the technology life cycle for the identified high-value areas. Among the six technology areas identified, Topic 1 (Underground Material Handling System) and Topic 4 (Underground Transportation System) showed significant patent activity from companies and research institutions in China, the United States, South Korea, and Germany compared to other countries. These areas have the top 10 patent applicants, accounting for 20.8% and 13.6% of all patent applications, respectively. Additionally, technology life cycle analytics revealed a growth trajectory for these identified areas, indicating their rapid expansion and high innovation potential. This study provides a data-driven methodology to develop a technology roadmap that offers valuable insights for researchers, engineers, and policymakers in the ULS industry and supports informed decision-making regarding the field’s future direction.

show abstract

Understanding Malaysian Public Opinion on Suicide through Sentiment Analysis and Topic Modeling of Reddit Posts

Kamaruddin,

Abdul-Rahman,

Wibowo

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Suicide is a global public health concern, with the World Health Organization (WHO) identifying it as the second leading cause of death among individuals aged from 15 to 29 years. In Malaysia, recent statistics indicate a 10% increase in suicide cases in 2023 compared to the previous year. Online forums, such as Reddit, have become platforms for sharing opinions on this matter. Extracting and automatically analyzing these discussions can provide valuable insights into public opinion concerning this issue. While the existing research primarily focuses on identifying suicidal ideation from posts, the work on discerning public opinion remains limited. This study scraped opinion posts on suicide from the Malaysian Reddit community. Sentiment Analysis (SA) was conducted using a lexicon-based sentiment analyzer and topic modeling was performed by deploying Latent Dirichlet Allocation (LDA). The analysis revealed the following insights: (1) predominantly negative sentiments were detected in the opinions, both overall and within identified topics and (2) topic modeling indicated two distinctive topics reflecting the different perspectives and concerns of the Muslim and non-Muslim communities. Specifically, the overall SA revealed that 58% of the posts were negative, 10% were neutral, and 32% were positive. Within the identified topics, the Muslim community expressed a notably higher percentage of negative sentiments at 64%, compared to the 50% found in the non-Muslim community. These findings offer evidence-based insights into public opinion regarding suicide in Malaysia, contributing to the understanding of societal perspectives on this critical issue.

show abstract

LDA Based Topic Modeling on Hospital Facebook Posts

Cited by 3 publications

References 13 publications

Corporate communication on social media: a case study before and during pandemic COVID-19

Corporate communication on social media: a case study before and during pandemic COVID-19

A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems

Understanding Malaysian Public Opinion on Suicide through Sentiment Analysis and Topic Modeling of Reddit Posts

Contact Info

Product

Resources

About