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1
PROTECT: A Pipeline for Propaganda Detection and Classification
Vorakitphan, Vorakit
;
Cabrio, Elena
;
Villata, Serena
In: CLiC-it 2021- Italian Conference on Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03417019 ; CLiC-it 2021- Italian Conference on Computational Linguistics, Jan 2022, Milan, Italy (2022)
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2
#Bittersweet: Positive, negative, and mixed emotions in twitter posts ...
Langbehn, Andrew
. - : Open Science Framework, 2022
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3
Evaluation computergestützter Verfahren der Emotionsklassifikation für deutschsprachige Dramen um 1800 ...
Schmidt, Thomas
;
Dennerlein, Katrin
;
Wolff, Christian
. - : Zenodo, 2022
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Evaluation computergestützter Verfahren der Emotionsklassifikation für deutschsprachige Dramen um 1800 ...
Schmidt, Thomas
;
Dennerlein, Katrin
;
Wolff, Christian
. - : Zenodo, 2022
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5
MULDASA: Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media
Ghadah Alwakid; Taha Osman; Mahmoud El Haj; Saad Alanazi; Mamoona Humayun; Najm Us Sama
In: Applied Sciences; Volume 12; Issue 8; Pages: 3806 (2022)
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6
Lexicon-Based vs. Bert-Based Sentiment Analysis: A Comparative Study in Italian
Rosario Catelli; Serena Pelosi; Massimo Esposito
In: Electronics; Volume 11; Issue 3; Pages: 374 (2022)
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7
A New Ontology-Based Method for Arabic Sentiment Analysis
Safaa M. Khabour; Qasem A. Al-Radaideh; Dheya Mustafa
In: Big Data and Cognitive Computing; Volume 6; Issue 2; Pages: 48 (2022)
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8
COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset
Aijaz Ahmad Reshi; Furqan Rustam; Wajdi Aljedaani; Shabana Shafi; Abdulaziz Alhossan; Ziyad Alrabiah; Ajaz Ahmad; Hessa Alsuwailem; Thamer A. Almangour; Musaad A. Alshammari; Ernesto Lee; Imran Ashraf
In: Healthcare; Volume 10; Issue 3; Pages: 411 (2022)
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9
Exploring Bidirectional Performance of Hotel Attributes through Online Reviews Based on Sentiment Analysis and Kano-IPA Model
Yanyan Chen; Yumei Zhong; Sumin Yu; Yan Xiao; Sining Chen
In: Applied Sciences; Volume 12; Issue 2; Pages: 692 (2022)
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10
Analysis of Destination Images in the Emerging Ski Market: The Case Study in the Host City of the 2022 Beijing Winter Olympic Games
Yuanxiang Peng; Ping Yin; Kurt Matzler
In: Sustainability; Volume 14; Issue 1; Pages: 555 (2022)
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11
Connecting Text Classification with Image Classification: A New Preprocessing Method for Implicit Sentiment Text Classification
Meikang Chen; Kurban Ubul; Xuebin Xu; Alimjan Aysa; Mahpirat Muhammat
In: Sensors; Volume 22; Issue 5; Pages: 1899 (2022)
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12
Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
Ziwen Gao; Zhiyi Li; Jiaying Luo; Xiaolin Li
In: Applied Sciences; Volume 12; Issue 5; Pages: 2707 (2022)
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13
Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches
Nabila Mohamad Sham; Azlinah Mohamed
In: Sustainability; Volume 14; Issue 8; Pages: 4723 (2022)
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14
How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures
Shuyue Huang; Lena Jingen Liang; Hwansuk Chris Choi
In: Sustainability; Volume 14; Issue 5; Pages: 2675 (2022)
Abstract:
Service failure is inevitable. Although empirical studies on the outcomes and processes of service failures have been conducted in the hotel industry, the findings need more exploration to understand how different segments perceive service failures and the associated emotions differently. This approach enables hotel managers to develop more effective strategies to prevent service failures and implement more specific service-recovery actions. For analysis, we obtained a nine-year (2010–2018) longitudinal dataset containing 1224 valid respondents with 73,622 words of textual content from a property affiliated with an international hotel brand in Canada. A series of text-mining and natural language processing (NLP) analyses, including frequency analysis and word cloud, sentiment analysis, word correlation, and TF–IDF analysis, were conducted to explore the information hidden in the massive amount of unstructured text data. The results revealed the similarities and differences between groups (i.e., men vs. women and leisure vs. business) in reporting service failures. We also carefully examined different meanings of words that emerged from the text-mining results to ensure a more comprehensive understanding of the guest experience.
Keyword:
gender
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group difference
;
purpose of stay
;
sentiment analysis
;
service failure
;
text-mining approach
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TF–IDF analysis
;
word correlation
;
word frequency analysis
URL:
https://doi.org/10.3390/su14052675
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15
Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media
Lal Khan; Ammar Amjad; Kanwar Muhammad Afaq; Hsien-Tsung Chang
In: Applied Sciences; Volume 12; Issue 5; Pages: 2694 (2022)
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16
How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media
Shan Lu; Lingbo Zhao; Lizu Lai; Congrong Shi; Wanyue Jiang
In: International Journal of Environmental Research and Public Health; Volume 19; Issue 3; Pages: 1822 (2022)
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17
TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels
Muhammad Imran; Umair Qazi; Ferda Ofli
In: Data; Volume 7; Issue 1; Pages: 8 (2022)
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18
Detecting Depression Signs on Social Media: A Systematic Literature Review
Rafael Salas-Zárate; Giner Alor-Hernández; María del Pilar Salas-Zárate; Mario Andrés Paredes-Valverde; Maritza Bustos-López; José Luis Sánchez-Cervantes
In: Healthcare; Volume 10; Issue 2; Pages: 291 (2022)
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19
A Novel Method of Generating Geospatial Intelligence from Social Media Posts of Political Leaders
Fahim Sufi; Musleh Alsulami
In: Information; Volume 13; Issue 3; Pages: 120 (2022)
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20
Using social media and personality traits to assess software developers' emotions ...
Silva, Leo
;
Castro, Marília Gurgel
;
Silva, Miriam Bernardino
. - : Zenodo, 2022
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