Passage-based Web text mining (poster session)

2000 ◽  
Author(s):  
Thanaruk Theeramunkong
Keyword(s):  
Text Mining ◽  
Poster Session ◽  
Web Text Mining

Web Text Mining

2016 ◽  
Author(s):  
Ricardo Baeza-Yates ◽  
Roi Blanco ◽  
Malú Castellanos
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Web Search ◽  
Internet Users ◽  
Entity Retrieval ◽  
Web Text Mining ◽  
Text Content ◽  
The Web

Web search has become a ubiquitous commodity for Internet users. This fact puts a large number of documents with plenty of text content at our fingertips. To make good use of this data, we need to mine web text. This triggers the two problems covered here: sentiment analysis and entity retrieval in the context of the Web. The first problem answers the question of what people think about a given product or a topic, in particular sentiment analysis in social media. The second problem addresses the issue of solving certain enquiries precisely by returning a particular object: for instance, where the next concert of my favourite band will be or who the best cooks are in a particular region. Where to find these objects and how to retrieve, rank, and display them are tasks related to the entity retrieval problem.

Ontos Solutions for Semantic Web: Text Mining, Navigation and Analytics

2007 ◽  
pp. 11-27
Author(s):  
Vladimir Khoroshevsky ◽  
Irina Efimenko ◽  
Grigory Drobyazko ◽  
Polina Kananykina ◽  
Victor Klintsov ◽  
...  
Keyword(s):  
Text Mining ◽  
Semantic Web ◽  
Web Text Mining

Web text mining using a hybrid system

2002 ◽  
Author(s):  
F. Hideo Fukuda ◽  
E.L.P. Passos ◽  
M. Aurelio Pacheco ◽  
L. Biondi Neto ◽  
J. Valerio ◽  
...  
Keyword(s):  
Text Mining ◽  
Hybrid System ◽  
Web Text Mining

scholarly journals Contemporary Chinese parents' needs and questions of parenting for young children: a Web text-mining approach.

2020 ◽  
Vol 25 (4) ◽  
Author(s):  
Huihua He ◽  
◽  
Si He ◽  
Yan Li ◽  
◽  
...  
Keyword(s):  
Text Mining ◽  
Young Children ◽  
Age Groups ◽  
Parental Knowledge ◽  
Policy Makers ◽  
Mainland Chinese ◽  
Care Givers ◽  
Chinese Parents ◽  
Web Text Mining ◽  
Contemporary Chinese

Introduction. The current study investigated characteristics of parenting needs and questions of Mainland Chinese parents of young children. Specifically, Web text-mining technology was used to identify themes of parenting needs and questions, and parents' emotional status hidden in their question texts. Method. Total of 921,483 questions that parents posted from the top five parenting Websites in China during a 36-month study period were collected. Results. Daily care is one of the most important topics that concerned parents. Contemporary Mainland Chinese parents tend to raise questions about parental knowledge and skills. Different themes of questions could also be identified from different care-givers and different age groups of young children. Conclusions. From a parenting-oriented perspective, contemporary Chinese parents asked pesonalised questions through the Internet frequently. The considerable needs of grandparenting emerged. Programme designers and social policy makers should empower and support young children's parents with their parental knowledge, skills and emotional competence.

scholarly journals Logical Intelligent Detection Algorithm of Chinese Language Articles Based on Text Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zihui Zheng
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Chinese Language ◽  
Detection Algorithm ◽  
Processing Technology ◽  
Experimental Comparison ◽  
Intelligent Detection ◽  
Web Text Mining

With the advent of the big data era and the rapid development of the Internet industry, the information processing technology of text mining has become an indispensable role in natural language processing. In our daily life, many things cannot be separated from natural language processing technology, such as machine translation, intelligent response, and semantic search. At the same time, with the development of artificial intelligence, text mining technology has gradually developed into a research hotspot. There are many ways to realize text mining. This paper mainly describes the realization of web text mining and the realization of text structure algorithm based on HTML through a variety of methods to compare the specific clustering time of web text mining. Through this comparison, we can also get which web mining is the most efficient. The use of WebKB datasets for many times in experimental comparison also reflects that Web text mining for the Chinese language logic intelligent detection algorithm provides a basis.

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