Public Opinion Analysis of the Transportation Policy Using Social Media Data: A Case Study on the Delhi Odd–Even Policy

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 Networkbased 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.

Extraction Method and Integration Framework for Perception Features of Public Opinion in Transportation

“…and to make a city-feature polarity map for travelers [20]. On top of that, Chakraborty applied four different dictionary-based methods, including Bing, to evaluate emotion from Twitter, and to grasp public will in the process of transportation policy implementation [21].…”

“…The latter is essentially a text classifier, which extracts sequence rules by training sequential texts with labels to identify text polarity [18]. However, not only does machine learning consume a lot of manual annotation, but the model obtained is only suitable for a specific field and is difficult to apply to different texts [21]. Consequently, based on the emotional analysis model, we chose to establish a dictionary.…”

Research on the Evolution Path of Public Opinion in Environmental Emergencies

“…In the research of public opinion stage, Lin Ping, Liu Guowei, Gu Yiran, Dai Jianhua and so on use the LED model [1] , Moran`s I index and optimal segmentation theory [2] and the modified HK model [3] to study and analyze the evolution process of various types of public opinion events. In the area of emotional analysis, Pranamesh Chakraborty et al used CoreNLP software to conduct emotional analysis, and built a public model of emotional changes in government decisions [4] . In the research of public opinion theory model, Jaewon Kim et al constructed a network analysis model to quantitatively analyze the evolution of public opinion by comparing and analyzing the network data extracted from Twitter [5] .…”

Comparing global news sentiment using hesitant linguistic terms

“…Public opinion on a wide range of issues is available on online news and social media sites. 1 Social media sites have been used to measure public opinion on public policies, such as education, 2 climate change, 3 air pollution, 4 economics, 5 and water conservation. 6 Furthermore, news articles are more than facts alone, 7 journalists control what is explained and how the story is framed 8,9 providing a peek into national consciousness.…”

“…https://www.cnn.com/2020/04/15/world/trump-who-funding-explainer-intl-hnk/index.html 2 https://edition.cnn.com/2020/04/14/politics/donald-trump-world-health-organization-funding-coronavirus/ index.html 3 https://www.who.int/about/what-we-do4 https://www.who.int/about/planning-finance-and-accountability/how-who-is-funded 5 https://thehill.com/policy/healthcare/overnights/439199-overnight-health-care-largest-private-insurancecompany-slams…”