Abstract
The development of mobile internet has led to a massive amount of data being generated from mobile devices daily, which has become a source for analyzing human behavior and trends in public sentiment. In this paper, we build a system called MoSa (Mobile Sentiment analysis) to analyze this data. In this system, sentiment analysis is used to analyze news comments on the THAAD (Terminal High Altitude Area Defense) event from Toutiao by employing algorithms to calculate the sentiment value of the comment. This paper is based on HowNet; after the comparison of different sentiment dictionaries, we discover that the method proposed in this paper, which use a mixed sentiment dictionary, has a higher accuracy rate in its analysis of comment sentiment tendency. We then statistically analyze the relevant attributes of the comments and their sentiment values and discover that the standard deviation of the comments’ sentiment value can quickly reflect sentiment changes among the public. Besides that, we also derive some special models from the data that can reflect some specific characteristics. We find that the intrinsic characteristics of situational awareness have implicit symmetry. By using our system, people can obtain some practical results to guide interaction design in applications including mobile Internet, social networks, and blockchain based crowdsourcing.
Highlights
With the development of mobile Internet, Android has become the world’s largest operating system since 2017, according to Statcounter
A large amount of data can be generated from mobile devices, such as data, if the users spent a lof of time using different mobile apps
We propose a new method of analyzing sentiment trends from comments made via mobile applications; We propose some specific models of mobil app comments to reflect characteristics of the public’s behavior; We propose a conjecture that the average word length of a sentiment dictionary should be less than 2.3 to classify the sentiment tendency of a short text
Summary
With the development of mobile Internet, Android has become the world’s largest operating system since 2017, according to Statcounter. The valuable data collected in this paper can help us to analyze comments from Toutiao, a famous mobile news app in China, about the THAAD event. In order to analyze the information and public trends from the mobile application using big data, we opted to utilize sentiment analysis. We propose a new method of analyzing sentiment trends from comments made via mobile applications; We propose some specific models of mobil app comments to reflect characteristics of the public’s behavior; We propose a conjecture that the average word length of a sentiment dictionary should be less than 2.3 to classify the sentiment tendency of a short text.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
