Lumin Zhang

Microblogging Sentiment Analysis Using Emotional Vector

This paper proposes a new approach to analyze public moods on certain events in micro-blogging. Combining with clinical psychology, we use emotional vector rather than traditional orientation to perform sentiment analysis. The emotional vector could constantly absorb new Internet emotional words with our algorithm and has hierarchical structure so as to do multi-level analysis. Experimental evaluations show that there is a strong correlation between bursty events and public moods, and sentiment analysis could implement effectively using the emotional vector proposed in this article.

Tracking the evolution of public concerns in social media

Microblogging is becoming a popular social media in recent years. Observations show that a large part of posts in microblogging were talking about public events occurred in the real world. Public concerns reflect interests and expectations of the mass for an event. Therefore, to understand and analyze of public concerns will help us to grasp an event, and predict its trend. This paper presents an evolution analysis method of public concerns for a special kind of post in microblogging, which can provides sufficient background information about an event by its attachments, e.g. a URL for details, a picture, or a video, etc. we called it expandable post. We use expandable posts to reconstruct the topic space. Their reposts are regarded as public concerns, and are located on the space. Thus, the task of tracking public concerns is transformed into tracking the movement of those reposts, and analyzing the relationships between them and their corresponding expandable posts on the topic space. The preliminary experiments on our dataset about H7N9 bird flu collected from Weibo, shows the effectiveness of our method.

Detecting real-time burst topics in microblog streams: how sentiment can help

Microblog has become an increasing valuable resource of up-to-date topics about what is happening in the world. In this paper, we propose a novel approach of detecting real-time events in microblog streams based on bursty sentiments detection. Instead of traditional sentiment orientation like positive, negative and neutral, we use sentiment vector as our sentiment model to abstract subjective messages which are then used to detect bursts and clustered into new events. Experimental evaluations show that our approach could perform effectively for online event detection. Although we worked with Chinese in our research, the technique can be used with any other language.

User-level sentiment evolution analysis in microblog

Peoples attitudes towards public events or products may change overtime, rather than staying on the same state. Understanding how sentiments change overtime is an interesting and important problem with many applications. Given a certain public event or product, a users sentiments expressed in microblog stream can be regarded as a vector. In this paper, we define a novel problem of sentiment evolution analysis, and develop a simple yet effective method to detect sentiment evolution in user-level for public events. We firstly propose a multidimensional sentiment model with hierarchical structure to model users complicate sentiments. Based on this model, we use FP-growth tree algorithm to mine frequent sentiment patterns and perform sentiment evolution analysis by Kullback-Leibler divergence. Moreover, we develop an improve Affinity Propagation algorithm to detect why people change their sentiments. Experimental evaluations on real data sets show that sentiment evolution could be implemented effectively using our method proposed in this article.

Microblog sentiment analysis based on emoticon networks model

With the repaid development of Internet and communication technologies, microblog has become a valuable social media for public sentiment analysis. Emoticons, strongly associated with subjectivity and sentiments, are also increasing popular for users to directly express their feelings, emotions and moods in microblog platforms. In this paper, we address the problem of public sentiment analysis by leveraging emoticons, and develop emoticon networks approaches. Based on large-scale corpus, we use FP-growth algorithm combining with retrieve distance to aggregate similar emoticons, and build emoticon networks model based on Mutual Information. Then, we propose a microblog orientation analysis framework for both emoticon messages and non-emoticon messages. Experimental evaluations show that our approach could perform effectively for microblog sentiment analysis. Although we worked with Chinese in our research, the technique can be used with any other language.

Event Evolution Analysis in Microblogging Based on a View of Public Opinion Field

Event evolution analysis, which focus on discovering underlying relationships among events by using methods of data mining on text corpus, is a meaningful and challenge problem. In recent years, more and more people began to express their opinion on public events though microblogging services. It makes that the microblogging corpus contains not only the facts related to the events, but also the public concerns. Therefore, we believe that the event evolution analysis in microblogging should take different approaches and perspectives with the state-of-the-arts. In this paper, we employ the concept of public opinion field, on which event information and public opinion in text corpus are distinguished. Based on this view, we focus on how does the public opinion affect the evolution of events, propose a method to measure the influence, and represent it on the event evolution graph. The preliminary experiments on our dataset about H7N9 bird flu collected from Weibo shows that our method can get consistent results with our intuitive feel, that illustrates the effectiveness of the method.

The data partition strategy based on hybrid range consistent hash in NoSQL database

With the development of Internet technology and Cloud Computing, more and more applications have to be confronted with the challenges of big data. NoSQL Database is fit to the management of big data because of the characteristics of high scalability, high availability and high fault-tolerance. The data partitioning strategy plays an important role in the NoSQL database. The existing data partitioning strategies will cause some problems such as low scalability, hot spot and low performance and so on. In this paper we proposed a new data partitioning strategy---HRCH, which can partitioning the data in a reasonable way. At last we use some experiments to verify the effectiveness of HRCH. It shows that the HRCH can improve the scalability of the system. It also can avoid the hot spot problem as far as possible. And it also can improve the parallel degree of processing to improve the systems performance in some processing.

Predicting the social influence of upcoming contents in large social networks

Online social networks, such as twitter and facebook, are continuously generating the new contents and relationships. To fully understand the spread of topics, there are some essential but remaining open questions. Why do some seemingly ordinary topics actually received widespread attention? Is it due to the attractiveness of the content itself, or social network structure plays a larger role in the dissemination of information? Can we predict the trend of information dissemination? Analyzing and predicting the influence and spread of up-coming contents is an interesting and useful research direction, and has brilliant perspective on web advertising and spam detection. For solving the problems, in this paper, a novel time series model has been proposed. In this model, the existing user-generated contents are summarized with a set of valued sequences. An early predictor is adopted for analyzing the topical/structural properties of series, and the influence of newly coming contents are estimated with the predictor. The empirical study conducted on large real data sets indicates that our model is interesting and meaningful, and our methods are effective and efficient in practice.

Do neighbor buddies make a difference in reblog likelihood?: an analysis on SINA weibo data

Reblogging, also known as retweeting in Twitter parlance, is a major type of activities in many online social networks. Although there are many studies on reblogging behaviors and potential applications, whether neighbors who are well connected with each other (called “buddies” in our study) may make a difference in reblog likelihood has not been examined systematically. In this paper, we tackle the problem by conducting a systematic statistical study on a large SINA Weibo data set, which is a sample of 135, 859 users, 10, 129, 028 followers, and 2, 296, 290, 930 reblog messages in total. To the best of our knowledge, this data set has more reblog messages than any data sets reported in literature. We examine a series of hypotheses about how essential neighborhood structures may help to boost the likelihood of reblogging, including buddy neighbors versus buddyless neighbors, traffic between buddy neighbors, activeness (i.e., the total number of blog messages a user sends), and the number of buddy triangles a user participates in. Our empirical study discloses several interesting phenomena that are not reported in literature, which may imply interesting and valuable new applications.