A sequence level latent topic modeling method for sentiment analysis via CNN based Diversified Restrict Boltzmann Machine

2016 ◽  
Author(s):  
Yu Zhou ◽  
Ruifeng Xu ◽  
Lin Gui
Keyword(s):  
Sentiment Analysis ◽  
Topic Modeling ◽  
Modeling Method ◽  
Boltzmann Machine ◽  
Latent Topic ◽  
Restrict Boltzmann Machine

scholarly journals Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: A Digital Topic Modeling Approach

2020 ◽  
Author(s):  
Qian Liu ◽  
Zequan Zheng ◽  
Jiabin Zheng ◽  
Qiuyi Chen ◽  
Guan Liu ◽  
...  
Keyword(s):  
Mass Media ◽  
Health Communication ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Modeling Method ◽  
Search Term ◽  
Public Attention ◽  
News Reports ◽  
The Mass Media ◽  
Novel Coronavirus

AbstractBackgroundIn December 2019, some COVID-19 cases were first reported and soon the disease broke out. As this dreadful disease spreads rapidly, the mass media has been active in community education on COVID-19 by delivering health information about this novel coronavirus.MethodsWe adopted the Huike database to extract news articles about coronavirus from major press media, between January 1st, 2020, to February 20th, 2020. The data were sorted and analyzed by Python software and Python package Jieba. We sought a suitable topic number using the coherence number. We operated Latent Dirichlet Allocation (LDA) topic modeling with the suitable topic number and generated corresponding keywords and topic names. We divided these topics into different themes by plotting them into two-dimensional plane via multidimensional scaling.FindingsAfter removing duplicates, 7791 relevant news reports were identified. We listed the number of articles published per day. According to the coherence value, we chose 20 as our number of topics and obtained their names and keywords. These topics were categorized into nine primary themes based on the topic visualization figure. The top three popular themes were prevention and control procedures, medical treatment and research, global/local social/economic influences, accounting for 32·6%, 16·6%, 11·8% of the collected reports respectively.InterpretationThe Chinese mass media news reports lag behind the COVID-19 outbreak development. The major themes accounted for around half the content and tended to focus on the larger society than on individuals. The COVID-19 crisis has become a global issue, and society has also become concerned about donation and support as well as mental health. We recommend that future work should address the mass media’s actual impact on readers during the COVID-19 crisis through sentiment analysis of news data.FundingNational Social Science Foundation of China (18CXW021)Evidence before this studyThe novel coronavirus related news reports have engaged public attention in China during the COVID-19 crisis. Topic modeling of these news articles can produce useful information about the significance of mass media for early health communication. We searched the Huike database, the most professional Chinese media content database, using the search term “coronavirus” for related news articles published from January 1st, 2020, to February 20th, 2020. We found that these articles can be classified into different themes according to their emphasis, however, we found no other studies apply topic modeling method to study them.Added value of this studyTo our knowledge, this study is the first to investigate the patterns of health communications through media and the role the media have played and are still playing in the light of the current COVID-19 crisis in China with topic modeling method. We compared the number of articles each day with the outbreak development and identified there’s a delay in reporting COVID-19 outbreak progression for Chinese mass media. We identify nine main themes for 7791 collected news reports and detail their emphasis respectively.Implications of all the available evidenceOur results show that the mass media news reports play a significant role in health communication during the COVID-19 crisis, government can strengthen the report dynamics and enlarge the news coverage next time another disease strikes. Sentiment analysis of news data are needed to assess the actual effect of the news reports.

Analyzing Brexit’s impact using sentiment analysis and topic modeling on Twitter discussion

2020 ◽  
Author(s):  
Sardar Haider Waseem Ilyas ◽  
Zainab Tariq Soomro ◽  
Ahmed Anwar ◽  
Hamza Shahzad ◽  
Ussama Yaqub
Keyword(s):  
Sentiment Analysis ◽  
Topic Modeling

scholarly journals Understanding the nature and scope of clinical research commentaries in PubMed

2019 ◽  
Vol 27 (3) ◽  
pp. 449-456
Author(s):  
James R Rogers ◽  
Hollis Mills ◽  
Lisa V Grossman ◽  
Andrew Goldstein ◽  
Chunhua Weng
Keyword(s):  
Clinical Research ◽  
Sentiment Analysis ◽  
Random Sample ◽  
Full Text ◽  
Topic Modeling ◽  
Critical Appraisal ◽  
Research Articles ◽  
Disease Specific ◽  
Research Studies

Abstract Scientific commentaries are expected to play an important role in evidence appraisal, but it is unknown whether this expectation has been fulfilled. This study aims to better understand the role of scientific commentary in evidence appraisal. We queried PubMed for all clinical research articles with accompanying comments and extracted corresponding metadata. Five percent of clinical research studies (N = 130 629) received postpublication comments (N = 171 556), resulting in 178 882 comment–article pairings, with 90% published in the same journal. We obtained 5197 full-text comments for topic modeling and exploratory sentiment analysis. Topics were generally disease specific with only a few topics relevant to the appraisal of studies, which were highly prevalent in letters. Of a random sample of 518 full-text comments, 67% had a supportive tone. Based on our results, published commentary, with the exception of letters, most often highlight or endorse previous publications rather than serve as a prominent mechanism for critical appraisal.

Analysis and Visualization Latent Topic on COVID-19 Vaccine Tweet use two-stage topic modeling (Preprint)

2021 ◽  
Author(s):  
Faizah Faizah ◽  
Bor-Shen Lin
Keyword(s):  
Topic Modeling ◽  
Public Perception ◽  
Latent Dirichlet Allocation ◽  
World Health ◽  
Two Stage ◽  
The Public ◽  
Global Pandemic ◽  
Difficult Time ◽  
Latent Topic ◽  
Latent Topics

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.

scholarly journals How is People's Awareness of “Biodiversity” Measured ?Using Sentiment Analysis and LDA Topic Modeling in the Twitter Discourse Space from 2010 to 2020

2021 ◽  
Author(s):  
Shimon Ohtani
Keyword(s):  
Sentiment Analysis ◽  
Topic Modeling ◽  
Data Science ◽  
Latent Dirichlet Allocation ◽  
Biological Diversity ◽  
Public Awareness ◽  
Convention On Biological Diversity ◽  
Emotion Lexicon ◽  
Aichi Biodiversity Targets ◽  
Do So

Abstract The importance of biodiversity conservation is gradually being recognized worldwide, and 2020 was the final year of the Aichi Biodiversity Targets formulated at the 10th Conference of the Parties to the Convention on Biological Diversity (COP10) in 2010. Unfortunately, the majority of the targets were assessed as unachievable. While it is essential to measure public awareness of biodiversity when setting the post-2020 targets, it is also a difficult task to propose a method to do so. This study provides a diachronic exploration of the discourse on “biodiversity” from 2010 to 2020, using Twitter posts, in combination with sentiment analysis and topic modeling, which are commonly used in data science. Through the aggregation and comparison of n-grams, the visualization of eight types of emotional tendencies using the NRC emotion lexicon, the construction of topic models using Latent Dirichlet allocation (LDA), and the qualitative analysis of tweet texts based on these models, I was able to classify and analyze unstructured tweets in a meaningful way. The results revealed the evolution of words used with “biodiversity” on Twitter over the past decade, the emotional tendencies behind the contexts in which “biodiversity” has been used, and the approximate content of tweet texts that have constituted topics with distinctive characteristics. While the search for people's awareness through SNS analysis still has many limitations, it is undeniable that important suggestions can be obtained. In order to further refine the research method, it will be essential to improve the skills of analysts and accumulate research examples as well as to advance data science.

scholarly journals Análise de discursos em notícias sobre homofobia, racismo e sexismo em comentários de portais brasileiros de notícias

2021 ◽  
Author(s):  
Lucas Rodrigues ◽  
Antonio Jacob Junior ◽  
Fábio Lobato
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Data Visualization ◽  
Language Processing ◽  
Topic Modeling ◽  
Hate Speech ◽  
Psychological Impact ◽  
Internet Service ◽  
General Law

Posts with defamatory content or hate speech are constantly foundon social media. The results for readers are numerous, not restrictedonly to the psychological impact, but also to the growth of thissocial phenomenon. With the General Law on the Protection ofPersonal Data and the Marco Civil da Internet, service providersbecame responsible for the content in their platforms. Consideringthe importance of this issue, this paper aims to analyze the contentpublished (news and comments) on the G1 News Portal with techniquesbased on data visualization and Natural Language Processing,such as sentiment analysis and topic modeling. The results showthat even with most of the comments being neutral or negative andclassified or not as hate speech, the majority of them were acceptedby the users.

scholarly journals Tracking patients healthcare experiences during the COVID-19 outbreak: Topic modeling and sentiment analysis of doctor reviews

2021 ◽  
Vol 9 (3A) ◽  
Author(s):  
Adnan M. Shah ◽  
◽  
Xiangbin Yan ◽  
Samia tariq ◽  
Syed Asad A. Shah ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Swot Analysis ◽  
Vaccine Development ◽  
Time Slice ◽  
Healthcare Organizations ◽  
Physician Rating ◽  
Physician Rating Website

Emerging voices of patients in the form of opinions and expectations about the quality of care can improve healthcare service quality. A large volume of patients’ opinions as online doctor reviews (ODRs) are available online to access, analyze, and improve patients’ perceptions. This paper aims to explore COVID-19-related conversations, complaints, and sentiments using ODRs posted by users of the physician rating website. We analyzed 96,234 ODRs of 5,621 physicians from a prominent health rating website in the United Kingdom (Iwantgreatcare.org) in threetime slices (i.e., from February 01 to October 31, 2020). We employed machine learning approach, dynamic topic modeling, to identify prominent bigrams, salient topics and labels, sentiments embedded in reviews and topics, and patient-perceived root cause and strengths, weaknesses, opportunities, and threats (SWOT) analyses to examine SWOT for healthcare organizations. This method finds a total of 30 latent topics with 10 topics across each time slice. The current study identified new discussion topics about COVID-19 occurring from time slice 1 to time slice 3, such as news about the COVID-19 pandemic, violence against the lockdown, quarantine process and quarantine centers at different locations, and vaccine development/treatment to stop virus spread. Sentiment analysis reveals that fear for novel pathogen prevails across all topics. Based on the SWOT analysis, our findings provide a clue for doctors, hospitals, and government officials to enhance patients’ satisfaction and minimize dissatisfaction by satisfying their needs and improve the quality of care during the COVID-19 crisis.

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