Blog Hot Topic Detection And Its Analysis On Public Opinion

Social websites have been important platforms where users from the samedomain express their feelings, owing to the simple operation. The online consensus isa representative opinion that the majority of users hold for an online topic. The onlineconsensus has a great impact on the physical society, and it is useful to detect hottopics and predict the trend of the consensus in order to keep the society harmonious.The two research goals are how to detect hot topics that users pay more attentionto from the dispersive and heterogeneous Web data and how to predict the trend of theonline consensus by analyzing online opinions. The hot topic detection methods oftenadopt the text mining technique and aim at detecting hot topics by understanding thecontent of the text and identifying the correlation between information. The data fromsocial websites represent the pattern of user behavior and user ideas. Furthermore, thedata can represent different meanings when such factors as user role, motivation, timeand context change. Hence, the data analysis strategy needs to take the characteristicsof users and the influence of the environment into consideration. The Wisdom Web ofThings (W2T) methodology is proposed to solve the intersectional problem betweenthe offline world and the online world. In order to correctly understand the user needto provide the right service, the W2T methodology emphasizes the factors related tohumans, organizes the data in the form of the network, and manages the dataaccording to the information granularity. Both hot topics and online consensus resultfrom the interaction among users in a community, and the research is based on theW2T methodology and draws on theories from the computer science, sociology, andjournalism to analyze the formation and development of blog hot topics and onlineconsensus. The important factors that determine or influence the topics and onlineconsensus are identified to detect hot topics and predict the evolutionary trend of theconsensus. The main work can be described as follows:1) A method of constructing the topic model based on user views is proposed. Asfor temporal online topics, the traditional topic detection methods often adopt the textclustering technique and consider each cluster as a topic. Hence, the topic modelbased on those methods can’t intuitively reflect the structure of a topic and thechanges of issues. In order to construct a hierarchical topic model, the proposedmethod emphasizes the information granularity of the semantic expression and the characteristics of temporal online topics, and focuses on the event to analyze thecomponents and evolution of the topic.2) A method of identifying the opinion leader in an online community is proposed.The virtual network is different from the physical society, and the evaluation measuretakes the formation mechanism of opinion leaders and behavioral characteristics intoaccount. On one hand, the number of relevant posts and the position in the socialnetwork are measured to assess the user influence on the topical spread. On the otherhand, the quality of a post and the influence on neighbors’ opinion making aremeasured to assess the user influence on the direction of online consensus.3) As for the method of constructing the topic model based on user views, amethod of blog hot topic detection is proposed. The life span of a temporal online hottopic often shows the drastic changes. However, the traditional methods oftenevaluate the topic hotness by counting the degree of the user participation, userattention, and topical novelty during a period of time. Hence, the number of replies,post publishers, opinions and opinion leaders within different time intervals are usedto evaluate the topical growth. Blog hot topics are consequently identified by countingthe duration, the degree of the topical growth, user participation, and topical novelty.4) An approach to hot topic detection based on bursty words is proposed.Although hot topics can be represented by hot words, different words have differenteffects on representing a topic. In order to clearly reflect a topic, the approach focuseson the burst of the correlation between words. The word networks within differenttime intervals are constructed according to the co-occurrence between words and thentopics are extracted from the related word network. Hot topics are identified byevaluating the burst of each topic. As far as the burst feature of a topic is concerned,the user behavior such as the post publication and the reply to a post is counted.5) An evolution model of online consensus based on the opinion leader’s guidingrole is proposed. The traditional opinion evolution models are constructed in a closedsocial network. In order to simulate the evolution of online consensus in a dynamicnetwork, the model attaches more importance to opinion leaders, and the sentiment ofan opinion leader, the context of the network and the characteristics of opinioncommunicators are assessed to predict the status of the leader in the next time interval.