List of Articles text mining

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  1 - Proposing a Model for Extracting Information from Textual Documents, Based on Text Mining in E-learning
  Somayeh Ahari
  As computer networks become the backbones of science and economy, enormous quantities documents become available. So, for extracting useful information from textual data, text mining techniques have been used. Text Mining has become an important research area that disco More

  As computer networks become the backbones of science and economy, enormous quantities documents become available. So, for extracting useful information from textual data, text mining techniques have been used. Text Mining has become an important research area that discoveries unknown information, facts or new hypotheses by automatically extracting information from different written documents. Text mining aims at disclosing the concealed information by means of methods which on the one hand are able to cope with the large number of words and structures in natural language and on the other hand allow handling vagueness, uncertainty and fuzziness. Text mining, referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text that high-quality information is typically derived through the patterns and processes. Moreover, text mining, also known as text data mining or knowledge discovery from textual databases, refers to the process of extracting patterns or knowledge from text documents. In this research, a survey of text mining techniques and applications in e-learning has been presented. During these studies, relevant researches in the field of e-learning were classified. After classification of researches, related problems and solutions were extracted. In this paper, first, definition of text mining is presented. Then, the process of text mining and its applications in e-learning domain are described. Furthermore, text mining techniques are introduced, and each of these methods in the field of e-learning is considered. Finally, a model for the information extraction by text mining techniques in e-learning domain is proposed. Manuscript profile
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  2 - Discover product defect reports from the text of users' online comments
  narges nematifard Muharram Mansoorizadeh mahdi sakhaei nia
  With the development of Web 2 and social networks, customers and users can share their opinions about different products They leave. These ideas can be used as a valuable resource to determine the position of the product and its success in marketing. Extracting the rep More

  With the development of Web 2 and social networks, customers and users can share their opinions about different products They leave. These ideas can be used as a valuable resource to determine the position of the product and its success in marketing. Extracting the reported shortcomings from the large volume of comments generated by users is one of the major problems in this field of research. By comparing the products of different manufacturers, customers and consumers express the strengths and weaknesses of the products in the form of positive and negative comments. Classification of comments based on positive and negative sensory words in the text does not lead to accurate results without reference to documents containing a defect report. Because defects are not reported solely in negative comments. It is possible for a customer to feel positive about a product and still report a defect in their opinion. Therefore, another challenge of this research field is the correct and accurate classification of opinions. To solve these problems and challenges, this article provides an effective and efficient way to extract comments containing product defect reports from users' online comments. For this purpose, stochastic forest classifiers were used to identify the defect report and the unattended thematic modeling technique used the Dirichlet hidden allocation to provide a summary of the defect report. Data from the Amazon website has been used to analyze and evaluate the proposed method. The results showed that random forest has an acceptable performance for defect reporting even with a small number of educational data. Results and outputs extracted from documents containing the defect report, including a summary of the defect report to facilitate manufacturers' decision making, finding patterns of the defect report in the text automatically, and discovering the aspects of the product that reported the most defects Related to themDemonstrates the ability of Dirichlet's latent allocation method. Manuscript profile
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  3 - Technology Watch” via “Information Technology
  Kiyarash Jahanpour
  Information is power, but knowledge is more powerful .information in patents and papers are good source of codified knowledge. Everyday a higher number of businesses make use of information from patents (as a main indicator of technology) and papers(as a principal More

  Information is power, but knowledge is more powerful .information in patents and papers are good source of codified knowledge. Everyday a higher number of businesses make use of information from patents (as a main indicator of technology) and papers(as a principal indicator of science) to see what products and systems are appearing in our globe. In an era of rapidly expanding digital content, overwhelming data available on the web and the high speed of S&T progress makes it difficult for experts to extract useful knowledge without powerful tools and they need to find new ways of reviewing and managing vast quantities of textual information. “Technology watch” is a collective voluntary process with which the companies work the information in an active manner. Purpose of “technology watch” is to gather process and integrate the technical information. TW has at least 3 objectives: Facilitating the innovation process; Easy and cost effective access to information and Answering to technological questions and problems. “Technology Watch” maintains awareness at all levels of global S&T through a combination of human-based overt and IT-based approaches for analyzing and tracking the myriad S&T outputs. Powerful IT-based techniques, such as text mining, now exist to identify and extract relevant data from the S&T literature and are especially useful in making sense out of disjointed and disparate data. Regarded by many as the next wave of knowledge discovery, text mining has very high commercial values. Manuscript profile
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  4 - Search Engine for Structured Event Retrieval from News Sources
  A. mirzaeiyan s. aliakbary
  Analysis of published news content is one of the most important issues in information retrieval. Much research has been conducted to analyze individual news articles, while most news events in the media are published in the form of several related articles. Event detect More

  Analysis of published news content is one of the most important issues in information retrieval. Much research has been conducted to analyze individual news articles, while most news events in the media are published in the form of several related articles. Event detection is the task of discovering and grouping documents that describe the same event. It also facilitates better navigation of users in news spaces by presenting an understandable structure of news events. With rapid and increasing growth of online news, the need for search engines to retrieve news events is felt more than ever. The main assumption of event detection is that the words associated with an event appear in the same time windows and similar documents. Accordingly, in this research, we propose a retrospective and feature-pivot method that clusters words into groups according to semantic and temporal features. We then use these words to produce a time frame and a human readable text description. The proposed method is evaluated on the All The News dataset, which consists of two hundred thousand articles from 15 news sources in 2016 and compared to other methods. The evaluation shows that the proposed method outperforms previous methods in terms of precision and recall. Manuscript profile