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Overwhelmed by Negative Emotions? Maybe You Are Being Cyber-bullied!
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In: Symposium On Applied Computing ; SAC 2019 - The 34th ACM/SIGAPP Symposium On Applied Computing ; https://hal.archives-ouvertes.fr/hal-02020829 ; SAC 2019 - The 34th ACM/SIGAPP Symposium On Applied Computing, Apr 2019, Limassol, Cyprus. ⟨10.1145/3297280.3297573⟩ (2019)
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| 43 |
Public Opinion Analysis of the Transportation Policy Using Social Media Data: A Case Study on the Delhi Odd–Even Policy
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In: Civil, Construction and Environmental Engineering Publications (2019)
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Abstract:
Twitter, a microblogging service, has become a popular platform for people to express their views and opinions on different issues. A sentiment analysis of the tweets can help in understanding the public opinion on different government decisions. This paper used Twitter data to extract the sentiments of people during the Phase 1 and Phase 2 of the odd–even policy implemented by the Delhi government to curb the air pollution and improve traffic flow. In this study, we used four different lexicon-based approaches: Bing, Afinn, National Research Council emotion lexicon, and Deep Recursive Neural Network-based Natural Language Processing software (CoreNLP) to extract sentiments from tweets and thereby assess overall public opinions. The daily trend obtained for each phase was normalized with the number of tweets and then compared using the Granger causality test. The causality test results showed that the trends obtained during the two phases were significantly different from each other. In particular, public sentiments were found to mostly turn negative during the later stage of the Phase 2 which indicates fading away of the public enthusiasm and positiveness towards the policy during the later stages of the policy implementation.
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Keyword:
Analysis; and Evaluation; Delhi; Odd–even; Policy Design; Sentiment analysis; Transportation Engineering; Twitter
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URL: https://lib.dr.iastate.edu/ccee_pubs/250
https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1254&context=ccee_pubs
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| 44 |
A T1OWA Fuzzy Linguistic Aggregation Methodology for Searching Feature-based Opinions.
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| 45 |
The effect of aggregation methods on sentiment classification in Persian reviews
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| 46 |
Fake news and propaganda: Trump’s democratic America and Hitler’s national socialist (Nazi) Germany
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In: Research outputs 2014 to 2021 (2019)
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| 47 |
Twitter Activity Of Urban And Rural Colleges: A Sentiment Analysis Using The Dialogic Loop
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In: FIU Electronic Theses and Dissertations (2019)
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| 48 |
On the Effect of Word Order on Cross-lingual Sentiment Analysis ; Sobre el efecto del orden de las palabras en el análisis de sentimiento crosslingüe
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| 49 |
Stroke Survivors on Twitter : Sentiment and Topic Analysis From a Gender Perspective
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| 50 |
Automated Soundtrack Generation for Fiction Books Backed by Lövheim’s Cube Emotional Model
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| 51 |
Combining speech-based and linguistic classifiers to recognize emotion in user spoken utterances
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| 53 |
Cross-lingual sentiment analysis for under-resourced languages
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In: TDX (Tesis Doctorals en Xarxa) (2019)
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| 55 |
Palavras com polarização positiva e polarização negativa em dois dicionários de língua portuguesa ; Words with positive polarity and negative polarity in two portuguese language dictionaries
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| 57 |
DECO-MWE: Building a Linguistic Resource of Korean Multiword Expressions for Feature-Based Sentiment Analysis
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In: 13th Workshop on Asian Language Resources ; https://hal.archives-ouvertes.fr/hal-01795167 ; 13th Workshop on Asian Language Resources, Kiyoaki Shirai, May 2018, Miyazaki, Japan. pp.14-20 ; http://www.jaist.ac.jp/project/alr13/index.html (2018)
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| 58 |
An Introduction to a Methodology of Implementing Korean Electronic Dictionaries for Corpus Analysis ; 코퍼스 분석을 위한 한국어 전자 사전 구축 방법론
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Nam, Jeesun. - : HAL CCSD, 2018. : Youk-Rack Publishing Company, 2018
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In: https://hal.archives-ouvertes.fr/hal-01795289 ; Youk-Rack Publishing Company, 2018, 979-11-6244-146-6 ; http://www.youkrack.co.kr/ (2018)
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| 59 |
Transforming big social data into forecasts - methods and technologies ; Transformer les big social data en prévisions - méthodes et technologies : Application à l'analyse de sentiments
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In: https://tel.archives-ouvertes.fr/tel-02060594 ; Ingénierie, finance et science [cs.CE]. Université d'Angers; Université Ibn Tofail. Faculté des sciences de Kénitra, 2018. Français. ⟨NNT : 2018ANGE0011⟩ (2018)
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A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter
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