1 |
Psychiatry on Twitter: Content Analysis of the Use of Psychiatric Terms in French
|
|
|
|
In: ISSN: 2561-326X ; JMIR Formative Research ; https://hal.archives-ouvertes.fr/hal-03614832 ; JMIR Formative Research, JMIR Publications 2022, 6 (2), pp.e18539. ⟨10.2196/18539⟩ ; https://formative.jmir.org/2022/2/e18539 (2022)
|
|
BASE
|
|
Show details
|
|
2 |
« “Twitta” “Intellectuelle” “Influenceuse” ? Être enseignante-chercheuse sur twitter »
|
|
|
|
In: ISSN: 1763-0061 ; EISSN: 1963-1812 ; Tracés : Revue de Sciences Humaines ; https://hal.archives-ouvertes.fr/hal-03592945 ; Tracés : Revue de Sciences Humaines, ENS Éditions, A paraître (2022)
|
|
BASE
|
|
Show details
|
|
3 |
Renouvellement paradigmatique dans l’analyse des discours numériques : le cas de la communication politique sur les RSN
|
|
|
|
In: ISSN: 2116-1747 ; Etudes de stylistique anglaise ; https://hal-amu.archives-ouvertes.fr/hal-03584927 ; Etudes de stylistique anglaise, Société de stylistique anglaise, Lyon, 2022, Renaissance(s)/Rebirth(s), ⟨10.4000/esa.4816⟩ ; https://journals.openedition.org/esa/4816 (2022)
|
|
BASE
|
|
Show details
|
|
4 |
Chapter 11. Consumer opinion about smoked bacon using Twitter and textual analysis: The challenge continues
|
|
|
|
In: Sensory Analysis for the Development of Meat Products ; https://hal-agrosup-dijon.archives-ouvertes.fr/hal-03575175 ; Sensory Analysis for the Development of Meat Products, Elsevier, pp.181-196, 2022, 9780128228326. ⟨10.1016/B978-0-12-822832-6.00013-8⟩ (2022)
|
|
BASE
|
|
Show details
|
|
5 |
#Bittersweet: Positive, negative, and mixed emotions in twitter posts ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
MULDASA: Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media
|
|
|
|
In: Applied Sciences; Volume 12; Issue 8; Pages: 3806 (2022)
|
|
Abstract:
The semantically complicated Arabic natural vocabulary, and the shortage of available techniques and skills to capture Arabic emotions from text hinder Arabic sentiment analysis (ASA). Evaluating Arabic idioms that do not follow a conventional linguistic framework, such as contemporary standard Arabic (MSA), complicates an incredibly difficult procedure. Here, we define a novel lexical sentiment analysis approach for studying Arabic language tweets (TTs) from specialized digital media platforms. Many elements comprising emoji, intensifiers, negations, and other nonstandard expressions such as supplications, proverbs, and interjections are incorporated into the MULDASA algorithm to enhance the precision of opinion classifications. Root words in multidialectal sentiment LX are associated with emotions found in the content under study via a simple stemming procedure. Furthermore, a feature–sentiment correlation procedure is incorporated into the proposed technique to exclude viewpoints expressed that seem to be irrelevant to the area of concern. As part of our research into Saudi Arabian employability, we compiled a large sample of TTs in 6 different Arabic dialects. This research shows that this sentiment categorization method is useful, and that using all of the characteristics listed earlier improves the ability to accurately classify people’s feelings. The classification accuracy of the proposed algorithm improved from 83.84% to 89.80%. Our approach also outperformed two existing research projects that employed a lexical approach for the sentiment analysis of Saudi dialects.
|
|
Keyword:
Arabic NLP; Arabic social media; lexical; Saudi dialects; sentiment analysis; Twitter
|
|
URL: https://doi.org/10.3390/app12083806
|
|
BASE
|
|
Hide details
|
|
11 |
Extracting Disaster-Related Location Information through Social Media to Assist Remote Sensing for Disaster Analysis: The Case of the Flood Disaster in the Yangtze River Basin in China in 2020
|
|
|
|
In: Remote Sensing; Volume 14; Issue 5; Pages: 1199 (2022)
|
|
BASE
|
|
Show details
|
|
12 |
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
|
|
|
|
In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
|
|
BASE
|
|
Show details
|
|
13 |
Social Media and the Pandemic: Consumption Habits of the Spanish Population before and during the COVID-19 Lockdown
|
|
|
|
In: Sustainability; Volume 14; Issue 9; Pages: 5490 (2022)
|
|
BASE
|
|
Show details
|
|
14 |
Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches
|
|
|
|
In: Sustainability; Volume 14; Issue 8; Pages: 4723 (2022)
|
|
BASE
|
|
Show details
|
|
15 |
Artificial Intelligent in Education
|
|
|
|
In: Sustainability; Volume 14; Issue 5; Pages: 2862 (2022)
|
|
BASE
|
|
Show details
|
|
16 |
eHealth Engagement on Facebook during COVID-19: Simplistic Computational Data Analysis
|
|
|
|
In: International Journal of Environmental Research and Public Health; Volume 19; Issue 8; Pages: 4615 (2022)
|
|
BASE
|
|
Show details
|
|
17 |
How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media
|
|
|
|
In: International Journal of Environmental Research and Public Health; Volume 19; Issue 3; Pages: 1822 (2022)
|
|
BASE
|
|
Show details
|
|
18 |
Knowledge Discovery from Large Amounts of Social Media Data
|
|
|
|
In: Applied Sciences; Volume 12; Issue 3; Pages: 1209 (2022)
|
|
BASE
|
|
Show details
|
|
19 |
Detecting Depression Signs on Social Media: A Systematic Literature Review
|
|
|
|
In: Healthcare; Volume 10; Issue 2; Pages: 291 (2022)
|
|
BASE
|
|
Show details
|
|
20 |
A Novel Method of Generating Geospatial Intelligence from Social Media Posts of Political Leaders
|
|
|
|
In: Information; Volume 13; Issue 3; Pages: 120 (2022)
|
|
BASE
|
|
Show details
|
|
|
|