scholarly journals Leveraging Text Mining Approach to Identify What People Want to Know About Mental Disorders From Online Inquiry Platforms

2021 ◽  
Vol 9 ◽  
Author(s):  
Soowon Park ◽  
Yaeji Kim-Knauss ◽  
Jin-ah Sim

Online inquiry platforms, which is where a person can anonymously ask questions, have become an important information source for those who are concerned about social stigma and discrimination that follow mental disorders. Therefore, examining what people inquire about regarding mental disorders would be useful when designing educational programs for communities. The present study aimed to examine the contents of the queries regarding mental disorders that were posted on online inquiry platforms. A total of 4,714 relevant queries from the two major online inquiry platforms were collected. We computed word frequencies, centralities, and latent Dirichlet allocation (LDA) topic modeling. The words like symptom, hospital and treatment ranked as the most frequently used words, and the word my appeared to have the highest centrality. LDA identified four latent topics: (1) the understanding of general symptoms, (2) a disability grading system and welfare entitlement, (3) stressful life events, and (4) social adaptation with mental disorders. People are interested in practical information concerning mental disorders, such as social benefits, social adaptation, more general information about the symptoms and the treatments. Our findings suggest that instructions encompassing different scopes of information are needed when developing educational programs.

2021 ◽  
Author(s):  
Jin-Ah Sim ◽  
Soowon Park

BACKGROUND Online inquiry platforms, which is where a person can anonymously ask questions, have become an important information source for those who are concerned about social stigma and discrimination that follow mental disorders. Therefore, examining what people inquire about regarding mental disorders would be useful when designing educational programs for communities. OBJECTIVE The present study aimed to examine the contents of the queries regarding mental disorders that were posted on online inquiry platforms. METHODS A total of 4,714 relevant queries from the two major online inquiry platforms were collected. We computed word frequencies, centralities, and latent Dirichlet allocation (LDA) topic modeling. RESULTS The words like symptom, hospital and treatment ranked as the most frequently used words, and the word my appeared to have the highest centrality. Results: Four topics exist according to the LDA, which are 1) understanding general symptoms, 2) disability grading system and welfare entitlement, 3) stressful life events, and (4) social adaptation with mental disorders. CONCLUSIONS People are interested in practical information concerning mental disorders, such as social benefits, social adaptation, and more general information about the symptoms and the treatments. Our findings suggest that instructions encompassing different scopes of information are needed when developing educational programs.


2012 ◽  
Vol 8 (1) ◽  
pp. 10-25 ◽  
Author(s):  
Stefan Sommer ◽  
Andreas Schieber ◽  
Kai Heinrich ◽  
Andreas Hilbert

In Social Commerce customers evolve to be an important information source for companies. Customers use the communication platforms of Web 2.0, for example Twitter, in order to express their sentiments about products or discuss their experiences with them. These sentiments can be very important for the development of products or the enhancement of marketing strategies. The research goal is to analyze customer sentiments in Twitter. The first step in the research is the detection of topics in Twitter entries which contain patterns of interest. For the topic detection, the authors use Latent Dirichlet Allocation for topic modeling. The authors found event based topics in the exemplary context of Sony’s 3D TV sets. In future work, the authors will implement sentiment analysis algorithms in order to determine sentiments in the entries corresponding to the detected topics.


2021 ◽  
pp. 1-16
Author(s):  
Ibtissem Gasmi ◽  
Mohamed Walid Azizi ◽  
Hassina Seridi-Bouchelaghem ◽  
Nabiha Azizi ◽  
Samir Brahim Belhaouari

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.


2020 ◽  
Vol 2 (3-4) ◽  
pp. 273-276
Author(s):  
Prakash B. Behere ◽  
Aniruddh P. Behere ◽  
Debolina Chowdhury ◽  
Amit B. Nagdive ◽  
Richa Yadav

Marriage can be defined as the state of being united as spouses in a consensual and contractual relationship recognized by law. The general population generally believes marriage to be a solution to mental illnesses. It can be agreed that mental disorders and marital issues have some relation. Parents of patients with psychoses expect that marriage is the solution to the illness and often approach doctors and seek validation about the success of the marriage of their mentally ill child, which is a guarantee no doctor can give in even normal circumstances. Evidence on sexual functioning in patients of psychosis is limited and needs further understanding. Studies show about 60%–70% women of the schizophrenia spectrum and illness to experience sexual difficulties. Based on available information, sexual dysfunction in population with psychosis can be attributed to a variety of psychosocial factors, ranging from the psychotic symptoms in itself to social stigma and institutionalization and also due to the antipsychotic treatment. Despite the decline in sexual activity and quality of life in general, it is very rarely addressed by both the treating doctor and by the patient themselves hence creating a lacuna in the patient’s care and availability of information regarding the illness’ pathophysiology. Patients become noncompliant with medications due to this undesirable effect and hence it requires to be given more attention during treatment. It was also found that paranoid type of schizophrenia patient had lower chances of separation than patients with other types of schizophrenia. The risk of relapse in cases with later age of onset of the disease, lower education, a positive family history of psychosis or a lower income increased more than other populations.


2021 ◽  
Vol 16 (4) ◽  
pp. 1042-1065
Author(s):  
Anne Gottfried ◽  
Caroline Hartmann ◽  
Donald Yates

The business intelligence (BI) market has grown at a tremendous rate in the past decade due to technological advancements, big data and the availability of open source content. Despite this growth, the use of open government data (OGD) as a source of information is very limited among the private sector due to a lack of knowledge as to its benefits. Scant evidence on the use of OGD by private organizations suggests that it can lead to the creation of innovative ideas as well as assist in making better informed decisions. Given the benefits but lack of use of OGD to generate business intelligence, we extend research in this area by exploring how OGD can be used to generate business intelligence for the identification of market opportunities and strategy formulation; an area of research that is still in its infancy. Using a two-industry case study approach (footwear and lumber), we use latent Dirichlet allocation (LDA) topic modeling to extract emerging topics in these two industries from OGD, and a data visualization tool (pyLDAVis) to visualize the topics in order to interpret and transform the data into business intelligence. Additionally, we perform an environmental scanning of the environment for the two industries to validate the usability of the information obtained. The results provide evidence that OGD can be a valuable source of information for generating business intelligence and demonstrate how topic modeling and visualization tools can assist organizations in extracting and analyzing information for the identification of market opportunities.


2021 ◽  
Vol 13 (5) ◽  
pp. 2876
Author(s):  
Anne Parlina ◽  
Kalamullah Ramli ◽  
Hendri Murfi

The literature discussing the concepts, technologies, and ICT-based urban innovation approaches of smart cities has been growing, along with initiatives from cities all over the world that are competing to improve their services and become smart and sustainable. However, current studies that provide a comprehensive understanding and reveal smart and sustainable city research trends and characteristics are still lacking. Meanwhile, policymakers and practitioners alike need to pursue progressive development. In response to this shortcoming, this research offers content analysis studies based on topic modeling approaches to capture the evolution and characteristics of topics in the scientific literature on smart and sustainable city research. More importantly, a novel topic-detecting algorithm based on the deep learning and clustering techniques, namely deep autoencoders-based fuzzy C-means (DFCM), is introduced for analyzing the research topic trend. The topics generated by this proposed algorithm have relatively higher coherence values than those generated by previously used topic detection methods, namely non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), and eigenspace-based fuzzy C-means (EFCM). The 30 main topics that appeared in topic modeling with the DFCM algorithm were classified into six groups (technology, energy, environment, transportation, e-governance, and human capital and welfare) that characterize the six dimensions of smart, sustainable city research.


2021 ◽  
Author(s):  
Faizah Faizah ◽  
Bor-Shen Lin

BACKGROUND The World Health Organization (WHO) declared COVID-19 as a global pandemic on January 30, 2020. However, the pandemic has not been over yet. Furthermore, in the first quartal of 2021, some countries face the third wave of the pandemic. During the difficult time, the development of the vaccines for COVID-19 accelerates rapidly. Understanding the public perception of the COVID-19 Vaccine according to the data collected from social media can widen the perspective on the state of the global pandemic OBJECTIVE This study explores and analyzes the latent topic on COVID-19 Vaccine Tweet posted by individuals from various countries by using two-stage topic modeling. METHODS A two-stage analysis in topic modeling was proposed to investigating people’s reactions in five countries. The first stage is Latent Dirichlet Allocation that produces the latent topics with the corresponding term distributions that facilitate the investigators to understand the main issues or opinions. The second stage then performs agglomerative clustering on the latent topics based on Hellinger distance, which merges close topics hierarchically into topic clusters to visualize those topics in either tree or graph views. RESULTS In general, the topic discussion regarding the COVID-19 Vaccine in five countries is similar. Topic themes such as "first vaccine" and & "vaccine effect" dominate the public discussion. The remarkable point is that people in some countries have some topic themes, such as "politician opinion" and " stay home" in Canada, "emergency" in India, and & "blood clots" in the United Kingdom. The analysis also shows the most popular COVID-19 Vaccine, which is gaining more public interest. CONCLUSIONS With LDA and Hierarchical clustering, two-stage topic modeling is powerful for visualizing the latent topics and understanding the public perception regarding the COVID-19 Vaccine.


Author(s):  
Amar Akbar ◽  
Imam Zainuri ◽  
Lilik Ma'rifatul Azizah ◽  
Kyle Dornhofer

Purpose - This article aims to give an opinion on the cause of still the case of pasung in Indonesia, physical restraint and reduction in people with mental illness (called pasung in indonesia), still found in indonesia, government program ” indonesia free of pasung” still can not erase indonesia from pasung. Design/methodology/approach -The approach to literature study causes the escape especially social stigma that occurs to make the case of the pipe still continues to exist Findings -The findings of many literature studies suggest that social stigma is a cause of social restraint in patients with severe psychiatric disorders Originality/value -The value of this study envolve Empowering people with mental disorders through social intervention can reduce the side effects of antipsychotic drugs and simultaneously help self-stigma in people with mental disorders


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