Modeling narrative structure and dynamics with networks, sentiment analysis, and topic modeling

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.

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The Instagram/Facebook ban on graphic self‐harm imagery: A sentiment analysis and topic modeling approach

Policy & Internet ◽

10.1002/poi3.272 ◽

2021 ◽

Author(s):

Hayden Smith ◽

William Cipolli

Keyword(s):

Sentiment Analysis ◽

Topic Modeling ◽

Modeling Approach ◽

Self Harm

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Texts of “internet confessions” as a source for training data set for the research on the sentiment-analysis field

Vestnik NSU Series Linguistics and Intercultural Communication ◽

10.25205/1818-7935-2019-17-3-71-82 ◽

2019 ◽

Vol 17 (3) ◽

pp. 71-82

Author(s):

Anastasia V. Kolmogorova

Keyword(s):

Sentiment Analysis ◽

Narrative Structure ◽

Training Data ◽

Data Set ◽

Financial Reports ◽

Technological Basis ◽

Self Image ◽

Textual Data ◽

Primary Advantage ◽

Multiclass Classifier

The article aims to analyze the validity of Internet confession texts used as a source of training data set for designing computer classifier of Internet texts in Russian according to their emotional tonality. Thus, the classifier, backed by Lövheim’s emotional cube model, is expected to detect eight classes of emotions represented in the text or to assign the text to the emotionally neutral class. The first and one of the most important stages of the classifier creation is the training data set selection. The training data set in Machine Learning is the actual dataset used to train the model for performing various actions. The internet text genres that are traditionally used in sentiment analysis to train two or three tonalities classifiers are twits, films and market reviews, blogs and financial reports. The novelty of our project consists in designing multiclass classifier that requires a new non-trivial training data. As such, we have chosen the texts from public group Overheard in Russian social network VKontakte. As all texts show similarities, we united them under the genre name “Internet confession”. To feature the genre, we applied the method of narrative semiotics describing six positions forming the deep narrative structure of “Internet confession”: Addresser – a person aware of her/his separateness from the society; Addressee – society / public opinion; Subject – a narrator describing his / her emotional state; Object – the person’s self-image; Helper – the person’s frankness; Adversary – the person’s shame. The above mentioned genre features determine its primary advantage – a qualitative one – to be especially focused on the emotionality while more traditional sources of textual data are based on such categories as expressivity (twits) or axiological estimations (all sorts of reviews). The structural analysis of texts under discussion has also demonstrated several advantages due to the technological basis of the Overheard project: the text hashtagging prevents the researcher from submitting the whole collection to the crowdsourcing assessment; its size is optimal for assessment by experts; despite their hyperbolized emotionality, the texts of Internet confession genre share the stylistic features typical of different types of personal internet discourse. However, the narrative character of all Internet confession texts implies some restrictions in their use within sentiment analysis project.

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

10.21203/rs.3.rs-922908/v1 ◽

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.

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

10.14210/cotb.v12.p467-474 ◽

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.

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

Journal of Engineering Research ◽

10.36909/jer.v9i3a.8703 ◽

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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