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Recently, people usually use the internet to obtain travel information, when they plan their travel. They especially want to obtain sightseeing spot information from reviews, but there are huge amounts of reviews of sightseeing spots. Users therefore cannot obtain important information from the reviews easily. As described herein, this paper aims to propose a system that automatically extracts and presents welcome news for sightseeing spots from reviews. This proposed Welcome-news is a “useful information” and “unexpected information” related to travel.

The flow for extracting Welcome-news from reviews is simple: A user inputs a sightseeing spot about which to get information; the system obtains reviews of the sightseeing spot and divides each sentence into reviews; the system extracts sentences including Welcome-news keyword(s), and the sentences become useful information; the system extracts unexpected information from useful information based on clustering, and it becomes Welcome-news; and the system presents all Welcome-news to the user.

This paper reports three findings: extraction of useful information for sightseeing spots based on Welcome-news keywords extracted by our experiment and using support vector machine (SVM); extraction of unexpected information for sightseeing spots by clustering; and automatic presentation of Welcome-news.

Numerous studies have extracted information from reviews based on some keywords. This proposed extraction of Welcome-news for travel not only uses keywords but also clusters based on topics. Furthermore, the proposed keywords include general keywords and unique keywords. The former appears for all kinds of sightseeing spots. The latter appears only for sightseeing spot. The authors extracted general keywords manually, and unique keywords using SVM.

This paper aims to propose a method for summarizing the topics of tweets using the Wikipedia category structure as common knowledge for supplementing the understanding of the Twitter user’s interests. There are many topics in the tweets, and the topics can be treated as a tree structure. However, when the topic hierarchy is constructed using existing hierarchal clustering approach, the granularity of tweet groups differs for each user. For summarizing the topics, identification of the topics which are heterogeneous and which are not is necessary because it is easy to understand if several groups are categorized into parent groups. However, if the group units are different for each user, a number of users’ interests cannot be summarized. If some tweets are grouped into the presidential election, and the others are into Donald Trump, there cannot be a count of how many users are interested in Donald Trump.

One solution of this issue is to construct topic structures by mapping one common tree structure. In this paper, a method is proposed for constructing the topic structure using the Wikipedia category tree similar to a common tree structure. The tweets are categorized, mapped to titles of articles in the Wikipedia category tree and then visualized as the hierarchal structure to the users.

The effectiveness of the proposed hierarchal topic structure is confirmed. In theme “politics”, the proposed method works well. The main reason is that there are many technical terms about politics in the Wikipedia categories and articles. It was found that a number of the terms of politics do not have multiple meanings, multiple semantics. However, in theme “sports”, the proposed method does not perform well. The main reason for this case is that there are a number of names of people present as topic names.

One important feature of the proposed method is that it is easy to grasp not only about the topics which are heterogeneous or homogeneous with each other but also consider the missing time when extracting topics. Another feature is that the topic structures for multiple users are easy to compare with each other.

This paper aims to propose a system that generates dialogue scenarios automatically in real time from Web news articles. Then, the authors used the Manzai metaphor, a form of Japanese traditional humorous comedy, in the system. The generated Manzai scenario consists of snappy patter and a humorous misunderstanding of dialogue based on the gap of our structure of funny points. The authors create communication robots to amuse people with the generated humorous robot dialogue scenarios.

The authors propose the following: how to generate funny dialogue-based scenario from Web news and Web intelligence, automatically? How to create direction of robots based on the pre-experiment? The authors conducted experiments from three viewpoints, namely, effectiveness of Manzai scenarios as content, effectiveness of Manzai-Robots as a medium and familiarity of Manzai-Robots.

In this paper, the authors find two points, namely, the new communication style called “human–robots implicit communication-and bridging the knowledge gap using Web intelligence, to communicate smoothly between humans and robots.

Numerous studies have examined communication robots that mutually communicate with people. However, for several reasons, communicating smoothly with people is difficult for robots. One reason is the problem of communication style. Another is knowledge gaps separating humans and robots. The authors propose a new communication style to solve the problems and designate the communication style based on dialogue between robots as “human-robot implicit communication”. The authors then create communication robots to communicate with people naturally, smoothly and with familiarity according to their dialogue.

Community‐type content that are social network services and blogs are maintained by communities of people. Occasionally, community members do not understand the nature of the content from multiple perspectives, and so the volume of information is often inadequate. The authors thus consider it necessary to present users with missing information. The purpose of this paper is to search for the content “hole” where users of community‐type content missed information.

The proposed content hole is defined as different information that is obtained by comparing community‐type content with other content, such as other community‐type content, other conventional web content, and real‐world content. The paper suggests multiple types of content holes and proposes a system that compares community‐type content with Wikipedia articles and identifies the content hole. The paper first identifies structured keywords from the community‐type content, and extracts target articles from Wikipedia using the keywords. It then extracts other related articles from Wikipedia using the link graph. Finally, it compares community‐type content with the articles in Wikipedia and extracts and presents content holes.

Information retrieval looks for similar data. In contrast, a content‐hole search looks for information that is different. This paper defines the type of content hole on the basis of viewpoints. The proposed viewpoints are coverage, detail, semantics, and reputation.

The paper proposes a system for extracting coverage content holes. The system compares community‐type content with Wikipedia and extracts content holes in the community‐type content.

The purpose of this paper is to propose a method of calculating the sentiment value of a tweet based on the emoticon role.

Classification of emoticon roles as four types showing “emphasis”, “assuagement”, “conversion” and “addition”, with roles determined based on the respective relations to sentiment of sentences and emoticons.

Clustering of users of four types based on emoticon sentiment.

Formalization, using regression analysis, of the relation of sentiment between sentences and emoticons in all roles.

The information of social media is not often written in ordinary web pages. Nevertheless, it is difficult to extract such information from social media because such services include so much information. Furthermore, various topics are mixed in social media communities. The authors designate such important and unique information related to social media as tip information. In this paper, they aim to propose a method to extract tip information that has been classified by topic from social networking services as a first step in extracting tip information from social media.

Themes of many kinds exist in a social media community because users write contents freely. Then the authors first detect the topics from the community and cluster the comment based on the topics. Subsequently, they extract tip information from each cluster. In this time, the tip information is include a user's experience and it has common important words.

The authors used an experiment to confirm that their proposed method can extract appropriate tip information from a community that a user specifies. The average precision is 69 per cent. A comparison of the authors' proposed method and baseline which is without detection of topic and clustering, the average precision obtained using the authors' proposed method is 18 per cent greater than the baseline.

The authors have three points to extract tip information from social media: topic detection and clustering from the social media using LDA method; extracting an author's actual experiences; and creation of a tip keyword dictionary from user experiments.

This aim of this paper is to elucidate rumor propagation on microblogs and to assess a system for collecting rumor information to prevent rumor-spreading.

We present a case study of how rumors spread on Twitter during a recent disaster situation, the Great East Japan earthquake of March 11, 2011, based on comparison to a normal situation. We specifically examine rumor disaffirmation because automatic rumor extraction is difficult. Extracting rumor-disaffirmation is easier than extracting the rumors themselves. We classify tweets in disaster situations, analyze tweets in disaster situations based on users' impressions and compare the spread of rumor tweets in a disaster situation to that in a normal situation.

The analysis results showed the following characteristics of rumors in a disaster situation. The information transmission is 74.9 per cent, representing the greatest number of tweets in our data set. Rumor tweets give users strong behavioral facilitation, make them feel negative and foment disorder. Rumors of a normal situation spread through many hierarchies but the rumors of disaster situations are two or three hierarchies, which means that the rumor spreading style differs in disaster situations and in normal situations.

The originality of this paper is to target rumors on Twitter and to analyze rumor characteristics by multiple aspects using not only rumor-tweets but also disaffirmation-tweets as an investigation object.