List of Articles متن‌کاوی

• Open Access Article
  • Abstract Page
  • Full-Text
  
  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
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Using web analytics in forecasting the stock price of chemical products group in the stock exchange
  amir daee Omid Mahdi Ebadati E. keyvan borna
  Forecasting markets, including stocks, has been attractive to researchers and investors due to the high volume of transactions and liquidity. The ability to predict the price enables us to achieve higher returns by reducing risk and avoiding financial losses. News plays More

  Forecasting markets, including stocks, has been attractive to researchers and investors due to the high volume of transactions and liquidity. The ability to predict the price enables us to achieve higher returns by reducing risk and avoiding financial losses. News plays an important role in the process of assessing current stock prices. The development of data mining methods, computational intelligence and machine learning algorithms have led to the creation of new models in prediction. The purpose of this study is to store news agencies' news and use text mining methods and support vector machine algorithm to predict the next day's stock price. For this purpose, the news published in 17 news agencies has been stored and categorized using a thematic language in Phoenician. Then, using text mining methods, support vector machine algorithm and different kernels, the stock price forecast of the chemical products group in the stock exchange is predicted. In this study, 300,000 news items in political and economic categories and stock prices of 25 selected companies in the period from November to March 1997 in 122 trading days have been used. The results show that with the support vector machine model with linear kernel, prices can be predicted by an average of 83%. Using nonlinear kernels and the quadratic equation of the support vector machine, the prediction accuracy increases by an average of 85% and other kernels show poorer results. ارسال Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  3 - Modeling of Electronic Word of Mouth Marketing Based on text mining User comments, A new approach On social commerce
  Elham Ramezani Ali Rajabzadeh Ghatary Vahid   Baradaran Maryam Shoar
  The purpose of this article is to present an Electronic Word of Mouth marketing model in social commerce Based on text mining User comments in sale sites. Due to the new research in this field and using the text mining method of user comments to express the variables of More

  The purpose of this article is to present an Electronic Word of Mouth marketing model in social commerce Based on text mining User comments in sale sites. Due to the new research in this field and using the text mining method of user comments to express the variables of this type of marketing model, this research is a kind of Exploratory Developmental Research. The method used in this research Is combination of qualitative and quantitative. In this regard,by studying previous researches As well as receiving, preprocessing and analyzing 11thousand Customers Online Comments In the case of digital products, Repetitive words with a positive label were selected Then, using Word2vec algorithm The variables of the Electronic Word of Mouth marketing model Were extracted using text mining technique. Fitting the model extracted, based on the comments of experts and users of internet sales sites in Iran with the help of a Questionnaire and analysed with statistical tools of least squares. The statistical sample of the second phase Due to the unlimited statistical population it was estimated according to Cochran's formula 384. In order to review and present the final model from the structural equations approach with SmartPLS software was used. The results show that customer interaction, message quality and Customer mental image will have positive and significant impact on the Platform and channel attractiveness of Electronic word of mouth marketing channel, Finally, these two variables will have a positive and significant impact on the Customer behavior and business brand. This model emphasizes new dimensions of variables of the Electronic Word of Mouth marketing model that can be helpful for business owners and marketers. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  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