Social media is commonly used by the public during election campaigns to express their opinions regarding different issues.
Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021.
We mapped the topics on five themes drawn from the sexual harassment literature and found that more than 50% of the topics were assigned to the unwanted sexual attention theme.
One of the challenges for text analysis in medical domains is analyzing large-scale medical documents.
Of the four strategies, unsupervised feature transformation (UFT) is a popular and efficient strategy to map the terms to a new basis in the document-term frequency matrix.
The Sex Discrimination and Gender harassment theme included stories about sex discrimination and gender harassment, such as sexist hostility behaviors ranging from insults and jokes invoking misogynistic stereotypes to bullying behavior.
Results: The text mining methods explored high-frequency neurologic DsSs and their trends and the relationships between them from 1955 to 2017.
This research emphasizes the value of social media analysis and proposes an analytical framework: Twitter Situational Awareness (TwiSA).
Also, our proposed approach can inform the development of computational strategies to identify the health-related information needs of other marginalized populations.
There are thousands of public libraries in the US, but no study has yet investigated the content of their social media posts like tweets to find their interests.
A lack of information exists about the health issues of lesbian, gay, bisexual, transgender, and queer (LGBTQ) people who are often excluded from national demographic assessments, health studies, and clinical trials.
Social networking sites such as Twitter have provided a great opportunity for organizations such as public libraries to disseminate information for public relations purposes.
This paper proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election.
This research investigates the application of fuzzy clustering as a DR method based on the UFT strategy to collapse BOW matrix to provide a lower-dimensional representation of documents instead of the words in a corpus.
The goal of this research is to analyze the characteristics of the general public's opinions in regard to diabetes, diet, exercise and obesity (DDEO) as expressed on Twitter.
This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-health related concerns in relations to the corpus of negative sentiments, regarding Diet Diabetes Exercise, and Obesity (DDEO).
The majority of medical documents and electronic health records (EHRs) are in text format that poses a challenge for data processing and finding relevant documents.