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A sentiment reporting framework for major city events: Case study on the China-United States trade war
Date
2020
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Abstract
Smart cities are conceptualized as a vehicle for sustainable urban development and a means to deliver
high quality of life for residents. One of the core functions of a smart city consists in the continuous
monitoring of events, assets and people and the use of this information and intelligence for the
streamlining of the city’s operations. Public opinion represents one type of intelligence of particular
importance and value. By monitoring public opinion, governments seek to understand prevalent views
about the current events and policies, as well as identify extreme views and trends that may represent
problematic situations or precursors to violent actions. Ultimately, maintaining a constant awareness of
public opinion means that authorities can better assess and predict public reactions in relation to
ongoing events, and thus take appropriate actions to maintain public safety. Due to the popular use of
social media to express sentiments and emotions about current events, social media content analysis has
been contemplated as a promising solution to capture public opinion. However, existing approaches take
a coarse-grained retrospective approach to social media content analysis. Furthermore, those approaches
suffer from the lack of scalability and efficiency, as they necessitate the collection and analysis of large
volumes of social media content (often millions of posts), to come up with relevant conclusions. In this
work, we address those limitations by proposing a novel framework for the real-time monitoring of
public opinion. To ensure efficiency and scalability, our framework focuses on the analysis of high impact
social media content generated by opinion leaders and their followers as means to offer in-depth insights
and sentiment intelligence reports about events, as they are occurring in real time. The proposed
framework was implemented and tested on data harvested from 52 economic opinion leaders, with a
focus on the China-US trade war as case study. The results show that the convolutional neural network
(CNN) classifier used for sentiment analysis yielded a classification accuracy of 86% when differentiating
between four sentiment categories: Support, strong support, dissent, and strong dissent. The Support
Vector Machine (SVM) classifier employed to perform in-depth emotional analysis attained an accuracy
of 82% when differentiating between five emotions: Angry, depressed, excited, happy, and worried.
Unlike existing retrospective social media analysis approaches that require the analysis of millions of
posts, our approach focuses on the analysis of high-impact social media content in real-time, thus
constituting an efficient, sustainable, and timely solution to public opinion monitoring.