A Text Mining Approach to Discover Real-Time Transit Events from Twitter

2018 ◽  
pp. 155-169
Author(s):  
Belén Arias Zhañay ◽  
Gerardo Orellana Cordero ◽  
Marcos Orellana Cordero ◽  
María-Inés Acosta Urigüen
Keyword(s):  
Text Mining ◽  
Real Time

scholarly journals Near-real-time drought impact assessment: a text mining approach on the 2018/19 drought in Germany

2020 ◽  
Vol 15 (10) ◽  
pp. 1040a9
Author(s):  
Mariana Madruga de Brito ◽  
Christian Kuhlicke ◽  
Andreas Marx
Keyword(s):  
Text Mining ◽  
Impact Assessment ◽  
Real Time ◽  
Drought Impact

scholarly journals Infodemia and COVID-19: a text mining analysis

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
W De Caro
Keyword(s):  
Public Health ◽  
Social Media ◽  
Content Analysis ◽  
Text Mining ◽  
Real Time ◽  
Public Perception ◽  
Health Authorities ◽  
Health Agencies ◽  
Use Of Social Media ◽  
Twitter Activity

Abstract Introduction Covid-19 epidemic lead a huge use of social media to comment and spread information from the widest sources. Infodemia looks at excessive amount of information circulating, which makes it difficult to orientate communities on a given topic due to the difficulty of identifying reliable sources. Using text mining analysis it is possible to identify what drives public conversation and impact of Covid-19. Methods Public perceptions in emergencies is traditionally measured with surveys. However, to have a global sight of the pandemia, Twitter represents a powerful tool which gives real-time monitoring of public perception. The study aimed to: 1) monitor the use of the terms “Covid-19” or “Coronarivus” over time; and 2) to conduct a specific text and sentiment analysis. Results Between January 10 and May 8, 2020, over 600 million tweets were retrieved. Of those 600.000 tweets were randomly selected, coded, and analyzed. About 10% of cases were identified as misinformation. Public figures, experts in public health, and virologists represent the most popular sources in comparison to the official government and health agencies. There is a positive correlation between Twitter activity peaks and COVID-19 infection peaks. Text mining analysis was carried out, as well as a content analysis, also in order to identify changing emotions and sentiments during time. This analysis, particularly during the lockdown, clearly shows that participation on social media can potentially have an effect on building social capital and social support. Conclusions This study confirms that using social media to conduct infodemic studies is an important area of development in public health arena. COVID-19 tweets were primarily used to disseminate information from credible sources, but were also a source of opinions, emotion and experiences. Tweets can be used for real-time content analysis and knowledge translation research, allowing health authorities to respond to public concerns. Key messages Social media is crucial for health information. Infodemia as new way for study health.

scholarly journals Tagger: BeCalm API for rapid named entity recognition

2017 ◽  
Author(s):  
Lars Juhl Jensen
Keyword(s):  
Open Access ◽  
Text Mining ◽  
Real Time ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
The Real ◽  
Practical Applications ◽  
Named Entity ◽  
Highly Efficient

AbstractMost BioCreative tasks to date have focused on assessing the quality of text-mining annotations in terms of precision of recall. Interoperability, speed, and stability are, however, other important factors to consider for practical applications of text mining. The new BioCreative/BeCalm TIPS task focuses purely on these. To participate in this task, I implemented a BeCalm API within the real-time tagging server also used by the Reflect and EXTRACT tools. In addition to retrieval of patent abstracts, PubMed abstracts, and Pub-Med Central open-access articles as required in the TIPS task, the BeCalm API implementation facilitates retrieval of documents from other sources specified as custom request parameters. As in earlier tests, the tagger proved to be both highly efficient and stable, being able to consistently process requests of 5000 abstracts in less than half a minute including retrieval of the document text.

scholarly journals Real Time Text Mining on Twitter Data

2017 ◽  
Vol 178 (3) ◽  
pp. 24-28
Author(s):  
Shilpy Gandharv ◽  
Vivek Richhariya ◽  
Vineet Richhariya
Keyword(s):  
Text Mining ◽  
Real Time ◽  
Twitter Data

Text Mining and Real-Time Analytics of Twitter Data: A Case Study of Australian Hay Fever Prediction

2018 ◽  
pp. 134-145 ◽  
Author(s):  
Sudha Subramani ◽  
Sandra Michalska ◽  
Hua Wang ◽  
Frank Whittaker ◽  
Benjamin Heyward
Keyword(s):  
Text Mining ◽  
Real Time ◽  
Hay Fever ◽  
Twitter Data

Real-Time Prediction of Employee Engagement Using Social Media and Text Mining

2018 ◽  
Author(s):  
Abbas Golestani ◽  
Mikhil Masli ◽  
N. Sadat Shami ◽  
Jennifer Jones ◽  
Abhilash Menon ◽  
...  
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Real Time ◽  
Employee Engagement ◽  
Time Prediction
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