Twitter is currently one of the most influential microblogging services on which users interact with messages. It is imperative to grasp the big picture of Twitter through analyzing its huge stream data. In this study, we develop a two-stage clustering method that automatically discovers coarse-grained topics from Twitter data. In the first stage, we use graph clustering to extract micro-clusters from the word co-occurrence graph. All the tweets in a micro-cluster share a fine-grained topic. We then obtain the time series of each micro-cluster by counting the number of tweets posted in a time window. In the second stage, we use time series clustering to identify the clusters corresponding to coarse-grained topics. We evaluate the computational efficacy of the proposed method and demonstrate its systematic improvement in scalability as the data volume increases. Next, we apply the proposed method to large-scale Twitter data (26 million tweets) about the COVID-19 Vaccination in Japan. The proposed method separately identifies the reactions to news and the reactions to tweets.

This paper is showing a time series topic life cycle extraction from millions of Tweets using our original community detection technique in bipartite networks. We suppose that the authors role that means who belong to what topics is important to extract quality topics from social media data. We already proposed the topic extraction method that considers the relationship between the authors and the words as bipartite networks and explores the authors role by forming clusters as topics. As the next step, this paper applies our method to the time series topic life cycle detection. We extract topics in different time slots and analyze the time series of topic transition using the coherence measure that expresses the semantic accuracy of topics. The paper demonstrates that our method can detect the topic life cycle such as the growth, the conflicts and so on over time from millions of Tweets.

Social media offers a wealth of insight into how significant topics such as the Great East Japan Earthquake, the Arab Spring, and the Boston Bombing affect individuals. The scale of available data, however, can be intimidating: during the Great East Japan Earthquake, over 8 million tweets were sent each day from Japan alone. Conventional word vector-based topic-detection techniques for social media that use Latent Semantic Analysis, Latent Dirichlet Allocation, or graph community detection often cannot extract appropriate topics from such a large volume of data with accuracy due to their space and time complexity. To alleviate this problem, we propose an effective topic extraction from millions of tweets based on community detection in bipartite networks. Our method is based on the bipartite community detection technique developed by Okamoto, one of the authors of this paper. The paper demonstrates our method effectiveness on social media analysis and identifies topics from millions of tweets after the Great East Japan Earthquake. To show our method's effectiveness, we compute the coherence measure that can evaluate the semantic accuracy and the running time, and compare the method with LDA that is the major topic model.

Feature selection is a useful tool for identifying which features, or attributes, of a dataset cause or explain phenomena, and improving the efficiency and accuracy of learning algorithms for discovering such phenomena. Consequently, feature selection has been studied intensively in machine learning research. However, advanced feature selection algorithms that can avoid redundant selection of features and can detect interacting features require heavy computation in general and hence are seldom used for big data analysis. To eliminate this limitation, we tried to improve the run-time performance of two of the most advanced feature selection algorithms known in the literature. We have developed two accurate and extremely fast algorithms, namely Super CWC and Super LCC. In experiments with multiple real datasets which are actually studied in big data research, we have demonstrated that our algorithms improve the performance of their original algorithms remarkably. For example, for two datasets, one with 15,568 instances and 15,741 features and another with 200,569 instances and 99,672 features, Super-CWC performed feature selection in 1.4 seconds and in 405 seconds, respectively. This is a remarkable improvement, because it is estimated that the original algorithms would need several hours to a few ten days to perform feature selection on the same datasets.

In this paper, the minutes of the monetary policy of the Bank of Japan, the central bank of Japan, for the year 1998 has been analyzed for the extraction of the topics concerning Asian financial crisis. The currency crisis started in Thailand in 1997 and spread toward many Asian countries. We analyzed the Monetary Policy Meeting minutes by text mining technologies. Especially we conducted topic extraction from the meeting minutes using Latent Dirichlet Allocation (LDA) model and the time series of the changes of extracted topic ratios are shown. From the analysis results, one topic which seems to be the Asian financial crisis related topic can be found. The topic ratio change curve clearly corresponds to the calm down of economic indices such as currency exchange ratios and market interest rates in the Asian countries.

After the East Japan Great Earthquake occurred in Japan on 11th March 2011, a large number of messages related to the earthquake were posted to social media web sites not only in Japan but also all over the world. Particularly, in Asian countries, a lot of topics concerned about the earthquake were observed. Exploring topics related to the earthquake on social media gains rich insights into the social contexts. The goal of this research is to analyze Asian people reactions to the East Japan Great Earthquake on social media. As the first target, this paper selected Thai language and analyzed how people reacted to the earthquake by comparing with reactions in Japan. This analysis also presented some characteristics of Thai society and culture.

This paper proposes a time series topic extraction method to investigate the transitions of people's needs after the East Japan Great Earthquake using latent semantic analysis. Our target data is a blog about afflicted people's needs provided by a non-profit organization in Tohoku, Japan. The method crawls blog messages, extracts terms, and forms document-term matrix over time. Then, the method adopts the latent semantic analysis and extract hidden topics (people's needs) over time. In our previous work, we already proposed the graph-based topic extraction method using the modularity measure. Our previous method could visualize topic structure transition, but could not extract clear topics. In this paper, to show the effectiveness of our proposed method, we provide the experimental results, and compare them with our previous method's results.

Once a disaster occurs, people discuss various topics in social media such as electronic bulletin boards, SNSs and video services, and their decision-making tends to be affected by discussions in social media. Under the circumstance, a mechanism to detect topics in social media has become important. This paper targets the East Japan Great Earthquake, and proposes a time series topic detection method based on modularity measure which shows the quality of a division of a network into modules or communities. Our proposed method clarifies emerging topics from social media messages by computing the modularity and analyzing them over time, and visualizes topic structures. An experimental result by actual social media data about the East Japan Great Earthquake is also shown.

Knowledge management has been a hot research issues for decades and resulted in many proposals and some systems that have been supporting information exchange in companies or societies. They failed however to become knowledge systems. With the advent of the internet the knowledge web is going to replace knowledge management. It creates its own challenges but take advantage of all web, information systems and software technology.

This paper proposes a method to discover consumer behavior from buzz marketing sites. For example, in 2009, the super-flu virus spawned significant effects on various product marketing domains around the globe. Using text mining technology, we found a relationship between the flu pandemic and the reluctance of consumers to buy digital single-lens reflex camera. We could easily expect more air purifiers to be sold due to flu pandemic. However, the reluctance to buy digital single-lens reflex cameras because of the flu is not something we would have expected. This paper applies text mining techniques to analyze expected and unexpected consumer behavior caused by a current topic like the flu. The unforeseen relationship between a current topic and products is modeled and visualized using a directed graph that shows implicit knowledge. Consumer behavior is further analyzed based on the time series variation of directed graph structures.

This paper presents a concept model for solving bond mathematics problems; it is the manner in which our knowledge concerning bond mathematics is organized into a cognitive architecture. We build a concept model for bond mathematics to achieve good organization and to integrate the knowledge for bond mathematics. The ultimate goal is to enable many students to understand the solution process without difficulty. Our concept model comprises entity-relationship diagrams, and using our concept models, students can integrate financial theories and mathematical formulas. This paper illustrates concept models for bond mathematics by showing concrete examples of word mathematics problems. It also describes our principles in developing the concept model and the descriptive power of our model.

A huge amount of multimedia content is available in the current Web environment. Streaming of video programs has become one of the popular Web-based information services. Automatic digest generation is an important application using video program streams. We have developed a digest making method called PDMS (Personal Digest Making Scheme). PDMS extracts significant scenes and constructs digests automatically using the video program meta data. In PDMS, only a successful play event such as a goal scored was considered significant, and a misplay event, such as a shot that did not score was not taken into account, although such an event may affect the game progress. This paper introduces a new concept of turning point analysis into PDMS. The turning point analysis is based on a winning probability for a sports programs. The winning probability indicates the probability of a home team beating an away team at the end of the game, given the current score and the time elapsed since the beginning of the game. Using the winning probability, we can more precisely evaluate the significance of each event, not merely a successful play but also a misplay. This paper presents turning point analysis for a soccer match. It also gives evaluation results of this extended PDMS, including turning point analysis for a recently broadcasted professional soccer match.