Self-Aware Contextual Behavior Analysis for Service Quality Assurance Over Social Networks

Social media allows people to share their ideologue through an efficient channel of communication. The social dialogues carry sentiment in expression regarding a particular social profile, trend, or topic. In our research, we have collected real-time user comments and feedbacks from Twitter portals of two food delivery services. This is followed by the extraction of the most prevalent contexts using natural language analytics. Further, our proposed algorithmic framework is used to generate a signed social network to analyze the product-centric behavioral sentiment. Analysis of sentiment with the fine-grained level about contexts gave a broader view to evaluate and perform contextual predictions. Customer behavior is analyzed, and the outcome is received in terms of positive and negative contexts. The results from our social behavioral model predicted the positive and negative contextual sentiments of customers, which can be further used to help in deciding future strategies and assuring service quality for better customer satisfaction.

Self-Aware Contextual Behavior Analysis for Service Quality Assurance over Social Networks

Social media allows people to share their ideologue through an efficient channel of communication. The social dialogues carry sentiment in expression regarding a particular social profile, trend, or topic. In our research, we have collected real-time user comments and feedbacks from Twitter portals of two food delivery services. This is followed by the extraction of the most prevalent contexts using natural language analytics. Further, our proposed algorithmic framework is used to generate a signed social network to analyze the product-centric behavioral sentiment. Analysis of sentiment with the fine-grained level about contexts gave a broader view to evaluate and perform contextual predictions. Customer behavior is analyzed, and the outcome is received in terms of positive and negative contexts. The results from our social behavioral model predicted the positive and negative contextual sentiments of customers, which can be further used to help in deciding future strategies and assuring service quality for better customer satisfaction.

Minimum Memory-Based Sign Adjustment in Signed Social Networks

In social networks comprised of positive (P) and negative (N) symmetric relations, individuals (nodes) will, under the stress of structural balance, alter their relations (links or edges) with their neighbours, either from positive to negative or vice versa. In the real world, individuals can only observe the influence of their adjustments upon the local balance of the network and take this into account when adjusting their relationships. Sometime, their local adjustments may only respond to their immediate neighbourhoods, or centre upon the most important neighbour. To study whether limited memory affects the convergence of signed social networks, we introduce a signed social network model, propose random and minimum memory-based sign adjustment rules, and analyze and compare the impacts of an initial ratio of positive links, rewire probability, network size, neighbor number, and randomness upon structural balance under these rules. The results show that, with an increase of the rewiring probability of the generated network and neighbour number, it is more likely for the networks to globally balance under the minimum memory-based adjustment. While the Newmann-Watts small world model (NW) network becomes dense, the counter-intuitive phenomena emerges that the network will be driven to a global balance, even under the minimum memory-based local sign adjustment, no matter the network size and initial ratio of positive links. This can help to manage and control huge networks with imited resources.

Exploiting Social Network Structure for Person-to-Person Sentiment Analysis

Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion. Such evaluations can be analyzed separately using signed social networks and textual sentiment analysis, but this misses the rich interactions between language and social context. To capture such interactions, we develop a model that predicts individual A’s opinion of individual B by synthesizing information from the signed social network in which A and B are embedded with sentiment analysis of the evaluative texts relating A to B. We prove that this problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss Markov random field, and we show that this implementation outperforms text-only and network-only versions in two very different datasets involving community-level decision-making: the Wikipedia Requests for Adminship corpus and the Convote U.S. Congressional speech corpus.

An Extend SIR Opinion Dynamic Model Base on Empirical Signed Network Mining

Relations between users on online social media sites often reflect a mixture of positive and negative interactions. The network composed by those positive and negative relations is called signed social network. We design a web crawler to collect the data base on a special web event of battle between Fang Zhouzi and Han Han. And we construct a signed social network with sentiment weighted relationships base on this empirical data. Under this empirical spread web structure, we construct an extended SIR spread model in such a signed social network with sentiment weighted relationships, and we study influence with the network factors of signed, directed and weighted on opinion spreading. Under this model, we could know the proportion of signed edges is most important factor to the spread result.