Tag Search System Using the Keyword Extraction and Similarity Evaluation

Interviews and questionnaires are the basis for collecting information about the opinions, concerns and needs of people. Analysis of those texts is crucial to understand the kansei of people. Text mining is an approach to discover useful and interesting patterns, knowledge and information from texts. This chapter contains two sections on text mining for beginners of it. The first section gives a brief survey of basic text mining techniques, such as keyword extraction, word graphs, clustering of texts and association rule mining. The second section demonstrates an example of text mining applied to interview analysis. Two text mining systems - the concept graph system and the matrix search system - are applied to analyze 2,409 remarks about products and services from 19 people. The analysis shows that text mining systems with a search function achieve interactive analysis of texts and an examination of various problems that we targeted.

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Text Mining for Analysis of Interviews and Questionnaires

Data Mining ◽

10.4018/978-1-4666-2455-9.ch072 ◽

2013 ◽

pp. 1390-1406 ◽

Cited By ~ 1

Author(s):

Yasuhiro Yamada ◽

Kanji Kato ◽

Sachio Hirokawa

Keyword(s):

Text Mining ◽

Association Rule Mining ◽

Keyword Extraction ◽

Rule Mining ◽

Search System ◽

Search Function ◽

Interactive Analysis ◽

The Matrix ◽

Graph System ◽

Interview Analysis

Interviews and questionnaires are the basis for collecting information about the opinions, concerns and needs of people. Analysis of those texts is crucial to understand the kansei of people. Text mining is an approach to discover useful and interesting patterns, knowledge and information from texts. This chapter contains two sections on text mining for beginners of it. The first section gives a brief survey of basic text mining techniques, such as keyword extraction, word graphs, clustering of texts and association rule mining. The second section demonstrates an example of text mining applied to interview analysis. Two text mining systems - the concept graph system and the matrix search system - are applied to analyze 2,409 remarks about products and services from 19 people. The analysis shows that text mining systems with a search function achieve interactive analysis of texts and an examination of various problems that we targeted.

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Privacy-Preserving RFID-Based Search System

Electronics ◽

10.3390/electronics10050599 ◽

2021 ◽

Vol 10 (5) ◽

pp. 599

Author(s):

Ji Young Chun ◽

Geontae Noh

Keyword(s):

Radio Frequency Identification ◽

Signal Strength ◽

Rfid Technology ◽

User Privacy ◽

Search System ◽

Rfid Tag ◽

Passive Tags ◽

Frequency Identification ◽

Privacy Issues ◽

Tag Search

The employment of mobile readers (or mobile phone collaborated with a Radio frequency identification (RFID) reader) opens a novel application for RFID technology. In particular, an RFID tag search system has been designed to find a particular tag in a group of tags using a mobile reader. Unfortunately, privacy infringement and availability issues in the search system have not been adequately addressed to date. In this paper, we propose a novel RFID tag search protocol that will enhance mobile reader user privacy while being able to operate under conditions of unstable connection to a central server. First, the proposed protocol preserves the privacy of mobile reader users. The privacy of the mobile reader user is at risk because the signal strength emitted from a mobile reader is much stronger than that from the tag, exposing the location of the mobile reader user and thus compromising the user’s privacy. Thus far, such privacy issues have been overlooked. The second issue is presented because of wireless connections that are either unreliable or too remote, causing a mobile reader to disconnect from the central server. The proposed protocol enables serverless RFID tag searches with passive tags, which obtain operating power from the mobile reader. In unstable environments, the protocol can successfully locate specific tags without any server.

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Keyword Extraction from TV Program Viewers’ Tweet Based on Neural Embedding Model

Fuzzy Systems and Data Mining VI - Frontiers in Artificial Intelligence and Applications ◽

10.3233/faia200714 ◽

2020 ◽

Author(s):

Taiga Kirihara ◽

Kazuyuki Matsumoto ◽

Minoru Yoshida ◽

Kenji Kita

Keyword(s):

Young People ◽

Real Time ◽

Experimental Results ◽

Keyword Extraction ◽

Search System ◽

Tv Program ◽

Key Phrases ◽

Live Action

In recent years, young people have not been watching television (TV) as much as they used to. This is mainly because a number of TV programs are very long and/or have limited viewing times. Recently, individuals have been actively posting live-action tweets on Twitter to comment on TV content while watching programs in real time. In this study, we propose a method for extracting key phrases related to the event scenes of TV programs using live tweets, and we propose a scene search system that aims at efficient TV program viewing. The experimental results indicated that the program contents were estimated with an error of approximately 5% to 10% with respect to the program time. In addition, the extracted key phrases were visualized for each event scene category using the t-SNE algorithm.

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