Mining Public Opinion on Transportation Systems Based on Social Media Data

sustainability Article Mining Public Opinion on Transportation Systems Based on Social Media Data Dawei Li 1,2,*, Yujia Zhang 1,2,* and Cheng Li 3 1 Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 210096, China 2 Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic, Southeast University, Nanjing 210096, China 3 China Academy of Transportation Sciences, No.240, Huixinli, Chaoyang District, Beijing 100029, China * Correspondence: [email protected] (D.L.); [email protected] (Y.Z.) Received: 10 June 2019; Accepted: 17 July 2019; Published: 25 July 2019 Abstract: Public participation plays an important role of traffic planning and management, but it is a great challenge to collect and analyze public opinions for traffic problems on a large scale under traditional methods. Traffic management departments should appropriately adopt public opinions in order to formulate scientific and reasonable regulations and policies. At present, while increasing degree of public participation, data collection and processing should be accelerated to make up for the shortcomings of traditional planning. This paper focuses on text analysis using large data with temporal and spatial attributes of social network platform. Web crawler technology is used to obtain traffic-related text in mainstream social platforms. After basic treatment, the emotional tendency of the text is analyzed. Then, based on the probabilistic topic modeling (latent Dirichlet allocation model), the main opinions of the public are extracted, and the spatial and temporal characteristics of the data are summarized. Taking Nanjing Metro as an example, the existing problems are summarized from the public opinions and improvement measures are put forward, which proves the feasibility of providing technical support for public participation in public transport with social media big data. Keywords: traffic planning; public participation; big data; content analysis; spatiotemporal properties 1. Introduction The transportation industry is the leading and basic industry of national economic and social development, and an important guarantee for social and economic development and improvement of people’s living standards. Transportation plays an important role in the whole social development. Raising the level of transportation management can not only promote employment, expand domestic demand, and promote social and economic development and urbanization, but also effectively improve the utilization of social resources, actively improve the environment, and facilitate people’s travel. In some areas, transportation administration departments have begun to try to introduce public participation. The public is the direct beneficiary and experiencer of transportation management and services. The introduction of public participation system in transportation management can better satisfy people’s interests and needs by drawing on public opinions and making decisions, supervising, and evaluating transportation planning. At present, the public participation system in China’s transportation management is still in early stages, and the transparency and openness of information such as transportation decision-making and supervision are insufficient. The mechanism of public participation is not perfect, the consciousness of public participation is weak, and the channels of participation are scarce. Therefore, actively exploring the countermeasures to improve the public participation system in transportation management is an urgent problem to be solved in the current transportation management. Sustainability 2019, 11, 4016; doi:10.3390/su11154016 www.mdpi.com/journal/sustainability Sustainability 2019, 11, x FOR PEER REVIEW 2 of 14 consciousness of public participation is weak, and the channels of participation are scarce. Therefore, Sustainabilityactively exploring2019, 11, 4016 the countermeasures to improve the public participation system in transportation2 of 15 management is an urgent problem to be solved in the current transportation management. InIn recent recent years,years, thethe emergenceemergence of of big big d dataata analysis analysis technology technology provides provides us uswith with new new ideas ideas and and methods to acquire and process traffic data [1]. The purpose of this paper is to apply the content methods to acquire and process traffic data [1]. The purpose of this paper is to apply the content analysis method of big data technology to traffic planning field, and put forward a set of processes analysis method of big data technology to traffic planning field, and put forward a set of processes and and methods combined with it, so as to provide feasible large data analysis methods for traffic methods combined with it, so as to provide feasible large data analysis methods for traffic planners. planners. This study focuses on text data that people often ignore, trying to obtain public views and This study focuses on text data that people often ignore, trying to obtain public views and opinions on opinions on the traffic system from comment texts, and then apply them to public participation in the traffic system from comment texts, and then apply them to public participation in transportation. transportation. In doing so, it can improve the extent of public participation, and speed up the Incollection doing so, and it can processing improve theof information. extent of public The participation, effective information and speed in uppublic the collectionopinion can and be processing made offull information. use of to analyze The eff ectivethe spatial information and temporal in public characteristics opinion can beand made make full up use forof the to blind analyze spots the that spatial andtraditional temporal planning characteristics cannot achieve. and make up for the blind spots that traditional planning cannot achieve. TheThe analysisanalysis processprocess is shown in in Figure Figure 1.1. Firstly, Firstly, public public opinions opinions are are collected collected from from common common socialsocial networknetwork platformsplatforms such as as microblog microblog by by using using open open platform platform SDK SDK and and API, API, and and do dobasic basic processingprocessing such such asas de-duplicationde-duplication and word word segmentation. segmentation. As As one one of of the the most most commonly commonly used used social social mediamedia platformsplatforms inin China,China, data in microblog have have large large mining mining and and research research value. value. However, However, as as shownshown in in the the next next section, section, the the previous previous studies studies of microblogof microblog data data in tra inffi trafficc field field rarely rarely focus focus on specific on textspecific semantic text semantic analysis analysis and sentiment and sentiment analysis, analysis, and therefore and therefore public public comments comments and feedbackand feedback cannot becannot collected. be collected. Thus, in Thus this, study,in this study we apply, we apply a content a content classification classification method method to extract to extract valuable valuable traffi c commentstraffic comments from the from whole the datawhole and data conduct and conduct emotional emotional orientation orientation analysis. analysis. The latent The Dirichletlatent allocationDirichlet a (LDA)llocation topic (LDA) model topic is usedmodel to is mine used the to topicmine ofthe text topic and of analyze text and the analyze subjective the subjective information behindinformation the text. behind Finally, the text. the Finally, temporal the temporal and spatial and characteristicsspatial characteristics of the of text the are text described. are described. After summarizingAfter summarizing the above the analysis above analysis results, correspondingresults, corresponding improvement improvement measures measures are put forward. are put forward. Data Collection Data Preprocessing Data Classification Data Modeling Feedback Data Basic Processing Screen Sentiment Duplication Optimization Text Analysis Data Related to Compress Spatiotemporal Analysis Website Traffic Analysis Results Comments LDA Topic Deletion Modeling Interaction Figure 1. PublicPublic o opinionpinion a analysisnalysis p procedurerocedure for for traffic traffi pclanning. planning. TheThe structurestructure ofof thisthis paper is as follows: T Thehe first first chapter chapter mainly mainly introduces introduces the the research research backgroundbackground and and significance significance of theof subject,the subject, and thenand liststhen the lists research the research content andcontent chapter and organization. chapter Theorganization. second chapter The second is about chapter the research is about status the research at home status and abroad,at home andand summarizesabroad, and summarizes the limitations andthe shortcomingslimitations and of publicshortcomings participation. of public Chapter participation. 3 is an introduction Chapter 3 tois the an methodology introduction oftocontent the analysis.methodology Chapter of content 4 takes analysis. Nanjing MetroChapter System 4 takes as Nanjing an example Metro to System collect publicas an example opinions to on collect Nanjing Metropublic System opinions for contenton Nanjing analysis Metro and System topic modeling. for content Chapter analysis 5 provides and topic suggestions modeling. and Chapter conclusions 5 forprovides the improvement suggestions measures and conclusions of Nanjing for metro

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