Investigation of the Influence of Twitter User Habits on Sentiment of Their Opinions towards Transportation Services
Bing Qi, Aaron M. Costin · 2019
Public opinion is a valuable tool for evaluating transportation services and planning. With the popularity of Web 2.0, there is an increasing number of people utilizing social media platforms (e.g. Twitter) to express their opinions. Therefore, social media is an inexpensive and abundant source of information that can be collected and utilized to enhance transportation services. Sentiment analysis, as a common social media data (SMD) processing technique, has been widely used to measure sentiment of user’s opinions on specific topics. However, previous research often neglected the influence of user habit in sentiment value analysis. For example, if one user utilizes social media to express only negative opinions, then their tweets expressing opinions on transportation service would also only be negative, which could cause interference with the evaluation result. The purpose of this research is to investigate the influence of user habit when conducting sentiment analysis on SMD in transportation field. The hypothesis is that user habits will have a positive correlation on sentiment value of selected tweets. To test this hypothesis, a case study is presented that collected tweets posted in Miami-Dade County from May 27th, 2017, to May 27th, 2018. The AFINN sentiment analysis method will be used to calculate the sentiment value of all of the tweets. Tweets relevant with personal opinions on transportation services (POTS) were extracted automatically using Naïve Bayes algorithm. All selected POTS tweets were given a revised sentiment value based on tweet itself, and a user average sentiment value based on this user’s historical tweets. The correlation analysis results of this study can test the correctness of hypothesis that user habits have great influence on sentiment value of selected tweets. Significantly, this innovative sentiment analysis method can make the SMD more reliable and valuable for transportation planning and decision-making process.