Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election

Social media has become an emerging alternative to opinion polls for public opinion collection, while it is still posing many challenges as a passive data source, such as structurelessness, quantifiability, and representativeness. Social media data with geotags provide new opportunities to unveil the geographic locations of users expressing their opinions. This paper aims to answer two questions: 1) whether quantifiable measurement of public opinion can be obtained from social media and 2) whether it can produce better or complementary measures compared to opinion polls. This research proposes a novel approach to measure the relative opinion of Twitter users towards public issues in order to accommodate more complex opinion structures and take advantage of the geography pertaining to the public issues. To ensure that this new measure is technically feasible, a modeling framework is developed including building a training dataset by adopting a state-of-the-art approach and devising a new deep learning method called Opinion-Oriented Word Embedding. With a case study of the tweets selected for the 2016 U.S. presidential election, we demonstrate the predictive superiority of our relative opinion approach and we show how it can aid visual analytics and support opinion predictions. Although the relative opinion measure is proved to be more robust compared to polling, our study also suggests that the former can advantageously complement the later in opinion prediction

Community-Based Behavioral Understanding of Mobility Trends and Public Attitude through Transportation User and Agency Interactions on Social Media in the Emergence of Covid-19

The increased availability of technology-enabled transportation options and modern communication devices (smartphones, in particular) is transforming travel-related decision-making in the population differently at different places, points in time, modes of transportation, and socio-economic groups. The emergence of COVID-19 made the dynamics of passenger travel behavior more complex, forcing a worldwide, unparalleled change in human travel behavior and introducing a new normal into their existence. This dissertation explores the potential of social media platforms (SMPs) as a viable alternative to traditional approaches (e.g., travel surveys) to understand the complex dynamics of people’s mobility patterns in the emergence of COVID-19. In this dissertation, we focus on three objectives. First, a novel approach to developing comparative infographics of emerging transportation trends is introduced by natural language processing and data-driven techniques using large-scale social media data. Second, a methodology has been developed to model community-based travel behavior under different socioeconomic and demographic factors at the community level in the emergence of COVID-19 on Twitter, inferring users’ demographics to overcome sampling bias. Third, the communication patterns of different transportation agencies on Twitter regarding message kinds, communication sufficiency, consistency, and coordination were examined by applying text mining techniques and dynamic network analysis. The methodologies and findings of the dissertation will allow real-time monitoring of transportation trends by agencies, researchers, and professionals. Potential applications of the work may include: (1) identifying spatial diversity of public mobility needs and concerns through social media platforms; (2) developing new policies that would satisfy the diverse needs at different locations; (3) introducing new plans to support and celebrate equity, diversity, and inclusion in the transportation sector that would improve the efficient flow of goods and services; (4) designing new methods to model community-based travel behavior at different scales (e.g., census block, zip code, etc.) using social media data inferring users’ socio-economic and demographic properties; and (5) implementing efficient policies to improve existing communication plans, critical information dissemination efficacy, and coordination of different transportation actors to raise awareness among passengers in general and during unprecedented health crises in the fragmented communication world

Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results