Proposal of Impression Mining from News Articles

Kumamoto, Tadahiko; Tanaka, Katsumi

doi:10.1007/11552413_129

Cited by 17 publications

(5 citation statements)

References 2 publications

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“…We use Japanese morphological analysis Juman [10]. At this time, we use some rules proposed by Kumamoto et al [9]. Table 5 presents an example of the rule.…”

Section: Calculating Value Of Sentiment Wordsmentioning

confidence: 99%

“…Then they model the word network as a spin model. Kumamoto et al [9] specically examine sentiments related to multi-dimensional emotions and then extract emotion from web news. The emotion model comprises two-dimensional sentiment, vectors sadglad and angrypleased, which are between 0 and 1.…”

Section: Related Workmentioning

confidence: 99%

“…Their system was designed using co-occurrence between the word in the news article and emotion words. Kumamoto et al [9], [8] propose a method of extracting form impressions how people feel as they see or hear the content. As described in this paper, we propose a method extracting not information from the originator's emotion, but from the information receiver's emotions.…”

Section: Related Workmentioning

confidence: 99%

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Words-of-wisdom search based on multi-dimensional sentiment vector

Takaoka

Nadamoto

2011

Proceedings of the 13th International Conference on Information Integration and Web-Based Applications and Services

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With the rapid advance of the Internet, everybody has become able to obtain information from it easily. However, there are no systems which are available to extract and present information suitable to a user's sentiment. We propose a system that searches for information based on a user's sentiment. As described in this this paper, we propose a words-of-wisdom search system as a rst step of the research. Specically, we rst propose a multi-dimensional sentiment vector based on Nakamura's proposed 10 categories of sentiments. Next, based on our experiment, we calculate the value of sentiment words included in wordsof-wisdom. Subsequently we calculate the sentiment value of words-of-wisdom using a value of sentiment words. We developed a prototype system and conducted experiments.

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“…We use Japanese morphological analysis Juman [10]. At this time, we use some rules proposed by Kumamoto et al [9]. Table 5 presents an example of the rule.…”

Section: Calculating Value Of Sentiment Wordsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Words-of-wisdom search based on multi-dimensional sentiment vector

Takaoka

Nadamoto

2011

Proceedings of the 13th International Conference on Information Integration and Web-Based Applications and Services

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“…[2]. Each entry in the impression dictionary corresponds to a word 2 and has a scale value and a weight.…”

Section: Content Termsmentioning

confidence: 99%

“…Each edition contains about 170,000 articles (200 MB), and the database contains two million or more articles in total 2. Negative forms of verbs and adjectives such as "kesa-nai (do not erase)" and "tanoshiku-nai (not be happy)" are treated as one word.…”

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confidence: 99%

u-PaV: Automatic Transformation of Web Content into TV-like Video Content for Ubiquitous Environment

Nadamoto

Kumamoto²,

Uwada

et al. 2006

7th International Conference on Mobile Data Management (MDM'06)

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We propose a system that automatically transforms web content into TV-like video content for ubiquitous environments. We call this system the ubiquitous/universal passive viewer (u-PaV). The u-PaV consists mainly of audio and visual components. The audio component uses synthesized speech to read out titles and lines extracted from the target web content. Simultaneously, the visual component of the uPaV presents the titles and lines to a user display through a ticker. Keywords and images extracted from the web content are animated on the display. A suitable background color is determined based on the overall impression of the content. The u-PaV synchronizes the ticker, animation, and speech. We introduce the u-PaV and explain how the keywords are extracted and how the impression value of the web content is determined. A test with 50 users showed that the u-PaV is easier to use and understand than browsing web content alone.

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