ORIGINAL_ARTICLE
Investigating the Relationship between Students’ Strategic Thinking Skills and Information Seeking Behavior among Undergraduate Students of Management in University of Tehran
This study aims to consider the relationship between undergraduate management students’ strategic thinkingskills and their information seeking behaviors. The statistical population for this study was 400 undergraduate students of management faculty in University of Tehran and the sample size was calculated to be 201 students using Cookran formula. In this study, survey methodology was applied and the data were analyzed using structural equation modeling, Pearson correlation, and multiple regression tests. Based on Pisapia’s model of strategic thinking and ACRL model of information seeking behavior , the results indicated that in among the different dimensions of strategic thinking, reframing thinking skill (leading to the formation of new values and getting away from the old clichés) has caused the highest change unit in information seeking behavior.
https://jitm.ut.ac.ir/article_65660_c60ba8ae50610a14ba3c7c5342668a04.pdf
2018-06-01
259
282
10.22059/jitm.2018.248131.2262
Information seeking behavior
Reflection thinking
Reframing thinking
Strategic thinking
Systems thinking
Mohammad
Abooyee Ardakan
abooyee@ut.ac.ir
1
Associate Prof. in Public Administration, Faculty of Management, Tehran University, Tehran, Iran
LEAD_AUTHOR
Fatemeh
Ghadyani
elaheghadyani@gmail.com
2
MA in Media Management, Faculty of Management, Tehran University, Tehran, Iran
AUTHOR
Tahereh
Alidadi
alidadi_tahereh@yahoo.com
3
MA in Urban Management, Faculty of Management, Tehran University, Tehran, Iran
AUTHOR
خسروجردی، محمود؛ علومی، طاهره؛ نقشینه، نادر؛ محسنی، نیکچهره (1388). نقش ابعاد شخصیت در رفتار اطلاعجویی دانشجویان کارشناسی ارشد دانشگاه تهران. فصلنامه علمی پژوهشی پژوهشگاه اطلاعات و مدارک علمی ایران، 24(3)،60-35.
1
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2
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3
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5
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6
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7
مکیزاده، فاطمه؛ بیگدلی، زاهد (1393). نظریۀ شناخت اجتماعی: رویکردی مؤثر در رفتارهای اطلاعاتی. پژوهشنامۀ کتابداری و اطلاع رسانی، 20 (4)، 147-131.
8
نوشینفرد، فاطمه، اسدی، مریم؛ حریری، نجلا (1392). تحلیل رفتار جستوجوی اطلاعات پژوهشگران حوزههای مختلف علوم از وب بر اساس سبکهای شناختی کلامی و تصویری اطلاعات. فصلنامۀ پردازش و مدیریت، 29 (4)، 1031-1007.
9
یعقوبی فرانی، احمد؛ حاجی هاشمی، زهرا؛ سعدی، حشمت اله (1395). تأثیر مهارتهای فناوری اطلاعات و ارتباطات مدیران بر ابعاد سازمان یادگیرنده در شرکتهای خدمات مشاورهای فنی و کشاورزی استان اصفهان. مدیریت فناوری اطلاعات، 8 (3)، 643-621.
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53
ORIGINAL_ARTICLE
A New Meta-heuristic Algorithm based on
Multi-criteria Decision Making to Solve
Community Detection Problem
Community detection is one of the most significant issues in the field of social networks. The main purpose of community detection is to partition the network in such a way that the relations between components of the network are dense. Because of the strong relations among network members in these partitions, you can consider them as a community. Many researchers have developed several algorithms to solve such a problem. Therefore, we present a genetic algorithm based on Topsis which is a multi-criteria decision making method (MCDM). The proposed algorithm uses Topsis to rank solutions based on modularity and modularity density which are two of the most well-known criteria in community detection problem. Thereafter, crossover and mutation operators are only applied on solutions ranked by Topsis. The performance of the proposed algorithm has been evaluated through comparing it against classical genetic algorithm and a greedy one. The results showed that the proposed algorithm outperforms the other two methods. Since the application of MCDM approach has not been reported in the related literature, this paper can be considered as a basis for future studies.
https://jitm.ut.ac.ir/article_63113_32b7d7223b0fe80e70cd49eeb2385757.pdf
2018-06-01
283
308
10.22059/jitm.2017.223145.1896
Community Detection
Genetic Algorithm
Optimization
social networks
TOPSIS
Vahid
Baradaran
v_baradaran@iau-tnb.ac.ir
1
Assistant Prof. of Industrial Engineering, Islamic Azad University, North Tehran Branch, Iran
LEAD_AUTHOR
Amir Hossein
Hosseinian
ah_hosseinian@iau-tnb.ac.ir
2
Ph.D. Candidate of Industrial Engineering, Islamic Azad University, North Tehran Branch, Iran
AUTHOR
Reza
Derakhshani
r_derakhshani@iau-tnb.ac.ir
3
Ph.D. Candidate of Industrial Engineering, Islamic Azad University North Tehran Branch, Iran
AUTHOR
اصغری اسکویی، محمدرضا؛ قاسمزاده، محمد (1395). کاربرد قواعد کشفی و الگوریتم ژنتیک در ساخت مدل ARMA برای پیشبینی سری زمانی. فصلنامۀ مدیریت فناوری اطلاعات، 8(1)، 26-1.
1
البرزی، محمود؛ پورزرندی، محمدابراهیم؛ خانبابایی، محمد (1389). بهکارگیری الگوریتم ژنتیک در بهینهسازی درختان تصمیمگیری برای اعتبارسنجی مشتریان بانک. فصلنامۀ مدیریت فناوری اطلاعات، 2(4)، 38- 23.
2
بهشتینیا، محمدعلی؛ فرازمند، ناهید (1394). ارائة سیستم پشتیبانی تصمیم نوین بهمنظور موازنة هزینه ـ انتشار دیاکسیدکربن گسسته در پروژههای ساخت: کاربردی از الگوریتم ژنتیک الگوبرداری. فصلنامۀ مدیریت فناوری اطلاعات، 7(1)، 48- 23.
3
بهشتینیا، محمدعلی؛ قهرمانی، مانی (1395). ارائة سیستم پشتیبانی تصمیم مبتنی بر الگوریتم ژنتیک (مطالعة موردی: زمانبندی در زنجیرة تأمین). فصلنامۀ مدیریت فناوری اطلاعات، 8(3)، 476- 455.
4
تقویفرد، محمدتقی؛ ساداتحسینی، فریبا؛ خانبابایی، محمد (1393). مدل رتبهبندی اعتباری هیبریدی با استفاده از الگوریتمهای ژنتیک و سیستمهای خبرة فازی (مطالعة موردی: موسسة مالی و اعتباری قوامین). فصلنامۀ مدیریت فناوری اطلاعات، 6(1)، 46-31.
5
حقیقی، الهام؛ منتظر، غلامعلی (1394). شناسایی عوامل مؤثر بر اعتمادسازی در شبکههای اجتماعی برخط بهکمک روش الکترة فازی. فصلنامۀ مدیریت فناوری اطلاعات، 7(4)، 740- 715.
6
رضایینور، جلال؛ لسانی، رضوان؛ زکیزاده، عاطفه؛ صفامجید، غدیر. (1393). بررسی شبکههای اجتماعی همکاری نویسندگی در حوزۀ فناوری اطلاعات با استفاده از تکنیکهای شبکههای اجتماعی. فصلنامۀ مدیریت فناوری اطلاعات، 6(2)، 250- 229.
7
فتحیان، محمد؛ حسینی، محمد (1393). بررسی تأثیر اجتماعات در تقویت رفتار خرید مشتریان. فصلنامۀ مدیریت فناوری اطلاعات، 6(3)، 454- 435.
8
کابارانزاده قدیم، محمدرضا؛ رفوگر آستانه، حسین (1388). طراحی یک سیستم پشتیبان تصمیمگیری (DSS) در مدیریت برای حل مسئلۀ تسطیح منابع در مدیریت پروژه با رویکرد الگوریتم ژنتیک (GA). فصلنامۀ مدیریت فناوری اطلاعات، 1(3)، 88- 69.
9
کیپور، اعظم؛ براری، مرتضی؛ شیرازی، حسین. (1393). ارائة روشی جدید برای پیشگویی پیوند بین رأسهای موجود در شبکههای اجتماعی. فصلنامۀ مدیریت فناوری اطلاعات، 6(3)، 486- 475.
10
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Rezaeenour, J., Lesani, R., Zakizadeh, A., & Safamajid, G. (2014). Evaluating Authorship Collaboration Networks in the Field of Information Technology Using Social Netwowk Techniques. Journal of Information Technology Management, 6(2), 229-250. (in Persian)
44
Shang, R., Bai, J., Jiao, L., & Jin, C., (2013) Community detection based on modularity and an improved genetic algorithm, Physica A: Statistical Mechanics and its Applications, 392(5), 1215-1231.
45
Shaqaqi, B., & Teymorpour, B., (2015). A new heuristic algorithm for modularity optimization in complex networks community detection. 11th International Industrial Engineering Conference, 7-8 January.
46
Shi, C., Yan, Z., Wang, Y., Cai, Y. & Wu, B. (2010). A Genetic Algorithm for Detecting Communities in Largescale Complex Networks. Advance in Complex System, 13(1), 3-17.
47
Taghavifard, M., Hosseini, F., & Khanbabaei, M. (2014). Hybrid credit scoring model using genetic algorithms and fuzzy expert systems Case study: Ghavvamin financial and credit institution. Journal of Information Technology Management, 6(1), 31-46. (in Persian)
48
Yue, Z., (2012). Extension of TOPSIS to determine weight of decision maker for group decision making problems with uncertain information. Expert Systems with Applications, 39(7), 6343-6350.
49
Zhang, H., Qiut, B., Giles, L., Foley, H. & Yen, J. (2007). An LDA-based Community Structure Discovery.Intelligence and Security Informatics, 400(2), 200-207.
50
ORIGINAL_ARTICLE
The Application of Machine Learning Algorithms for Text Mining based on Sentiment
Analysis Approach
Classification of the cyber texts and comments into two categories of positive and negative sentiment among social media users is of high importance in the research are related to text mining. In this research, we applied supervised classification methods to classify Persian texts based on sentiment in cyber space. The result of this research is in a form of a system that can decide whether a comment which is published in cyber space such as social networks is considered positive or negative. The comments that are published in Persian movie and movie review websites from 1392 to 1395 are considered as the data set for this research. A part of these data are considered as training and others are considered as testing data. Prior to implementing the algorithms, pre-processing activities such as tokenizing, removing stop words, and n-germs process were applied on the texts. Naïve Bayes, Neural Networks and support vector machine were used for text classification in this study. Out of sample tests showed that there is no evidence indicating that the accuracy of SVM approach is statistically higher than Naïve Bayes or that the accuracy of Naïve Bayes is not statistically higher than NN approach. However, the researchers can conclude that the accuracy of the classification using SVM approach is statistically higher than the accuracy of NN approach in 5% confidence level.
https://jitm.ut.ac.ir/article_65661_c8b4e78bb9acd81d32aaae090dd3eee4.pdf
2018-06-01
309
330
10.22059/jitm.2017.215513.1807
Naïve bayes
neural network
Sentiment analysis
Support vector machine
Text mining
Reza
Samizade
rsamizadeh@alzahra.ac.ir
1
Assistant Prof. of Industrial Engineering, Alzahra University, Tehran, Iran
AUTHOR
Elnaz
Mahmoudi Saeid Abad
mahmoudi.fe88@gmail.com
2
MSc. Student of Industrial Engineering, Alzahra University, Tehran, Iran
LEAD_AUTHOR
اسماعیلی، مهدی (1391). مفاهیم و تکنیکهای دادهکاوی، تهران، نیاز دانش.
1
نیکنام، فرزاد؛ نیک نفس، علی اکبر (1395). بهبود روشهای متنکاوی در کاربرد پیشبینی بازار با استفاده از الگوریتمهای انتخاب نمونۀ اولیه. فصلنامۀ علمی ـ پژوهشی مدیریت فناوری اطلاعات، 8 (2)، 432- 415.
2
References
3
Aggarwal, C. C., & Zhai, C. (Eds.). (2012). Mining text data. Springer Science & Business Media.
4
Bhadane, C., Dalal, H., & Doshi, H. (2015). Sentiment analysis: measuring opinions. Procedia Computer Science, 45, 808-814.
5
Esmaili, M. (2012). Concepts and techniques of data mainig.Niaz Danesh Perss, Tehran. (in Persian)
6
Gao, K., Xu, H., & Wang, J. (2015). A rule-based approach to emotion cause detection for Chinese micro-blogs. Expert Systems with Applications, 42(9), 4517-4528.
7
He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464-472.
8
Irfan, R., King, C. K., Grages, D., Ewen, S., Khan, S. U., Madani, S. A., ... & Tziritas, N. (2015). A survey on text mining in social networks. The Knowledge Engineering Review, 30(2), 157-170.
9
Jeyapriya, A., & Selvi, C. K. (2015, February). Extracting aspects and mining opinions in product reviews using supervised learning algorithm. In Electronics and Communication Systems (ICECS), 2015 2nd International Conference on (pp. 548-552). IEEE.
10
Jotheeswaran, J., & Kumaraswamy, Y. S. (2013). Opinion mining using decision tree based feature selection through manhattan hierarchical cluster measure. Journal of Theoretical & Applied Information Technology, 58(1), 72-80.
11
Kennedy, A., & Inkpen, D. (2006). Sentiment classification of movie reviews using contextual valence shifters. Computational intelligence, 22(2), 110-125.
12
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113.
13
Moraes, R., Valiati, J. F., & Neto, W. P. G. (2013). Document-level sentiment classification: An empirical comparison between SVM and ANN. Expert Systems with Applications, 40(2), 621-633.
14
Mosley Jr, R. C. (2012). Social media analytics: Data mining applied to insurance Twitter posts. In Casualty Actuarial Society E-Forum (Vol. 2, p. 1).
15
Niknam, F., Niknafas, A.A. (2016). Improving Text Mining Methods in Market Prediction via Prototype Selection Algorithms. Jornal of Information Technology Management, 8(2), 415-434. (in Persain)
16
Pradhan, V. M., Vala, J., & Balani, P. (2016). A survey on Sentiment Analysis Algorithms for opinion mining. International Journal of Computer Applications, 133(9), 7-11.
17
Ravichandran, M., & Kulanthaivel, G. (2014). Twitter Sentiment Mining (TSM) framework based learners emotional state classification and visualization for e-learning system. Journal of Theoretical & Applied Information Technology, 69(1), 84-90.
18
Smeureanu, I., & Bucur, C. (2012). Applying supervised opinion mining techniques on online user reviews. Informatica economica, 16(2), 81-91.
19
Vinodhini, G., & Chandrasekaran, R. M. (2012). Sentiment analysis and opinion mining: a survey. International Journal, 2(6), 282-292.
20
Xu, K., Liao, S. S., Li, J., & Song, Y. (2011). Mining comparative opinions from customer reviews for Competitive Intelligence. Decision support systems, 50(4), 743-754.
21
ORIGINAL_ARTICLE
Identification of Learning Management Systems Functional Areas and Limitations (Case Study:
E-Learning Center of University of Tehran)
Currently, ICT and educational processes are experiencing development and innovation. This new trend will help promote educational technology and enhance innovations regarding educational planning. E-learning is considered as one of the most prominent ICT applications across the world. Advantages of virtual learning have entailed daily usage in various universities. Learning management systems are specific web-based systems to manage, track students, define courses, and evaluate the learners. However, these systems may involve inefficiencies and disadvantages as well. This paper attempts to identify the LMS functional areas in University of Tehran based on a specific conceptual framework and to present the relevant issues and problems for each dimension. The data for the present study were collected using focused group interviews, system observations. The researchers also compared the documents and the university system with that of other universities. The results of the theme analysis indicated that “communication” and “system cooperation” dimensions are involved with more important problems and issues. The researchers believe that the main issues are due to the test modules, evaluations, and systemic and underlying databases.
https://jitm.ut.ac.ir/article_63294_3c12029666f9bc2eb073c580886b8b5c.pdf
2018-06-01
331
354
10.22059/jitm.2017.219238.1849
E-learning
Functional areas
Higher Education
Learning management system
Leraning processes
Ali Akbar
Farhangi
aafrhang@ut.ac.ir
1
Prof. in Management Department, University of Tehran, Tehran, Iran
AUTHOR
Hamidreza
Yazdani
hryazdani@ut.ac.ir
2
Assistant Prof., Dep. of Management, University of Tehran, Tehran, Iran
AUTHOR
Maryam
Haghshenas
m_haghshenas@ut.ac.ir
3
Ph.D. Candidate in Media Management, University of Tehran, Tehran, Iran
LEAD_AUTHOR
احمدی، حسن؛ احمدی، شهربانو؛ بیون، کاظم (1393). بررسی تأثیر آموزش الکترونیکی بر خلاقیت دانشآموزان پایۀ دوم راهنمایی در آموزش و پرورش شهر اصلاندوز (استان اردبیل). مجموعه مقالات نهمین کنفرانس سالانۀ یادگیری الکترونیکی، تهران، دانشگاه خوارزمی با همکاری انجمن یادگیری الکترونیکی ایران (یادا)، 20-21 اسفند1393.
1
آتشک، محمد (1386). مبانی نظری و کاربردی یادگیری الکترونیکی. فصلنامۀ پژوهش و برنامهریزی در آموزش عالی، ۱۳ (1)، ۱۳۵-۱۵۶.
2
حقشناس، مریم؛ خادمی، مریم (1390). ارتقای سطح یادگیری با ردهبندی ابزارهای نرمافزاری. همایش منطقهای یافتههای نوین در علوم کامپیوتر. آموزشکدۀ فنی حرفهای سما واحد بروجرد، 3 اسفندماه 1390.
3
زارعی زوارکی، اسماعیل؛ بدریان، مرضیه (1390). سنجش و ارزشیابی آموزش الکترونیکی: گزارش موردی از دورۀ آموزش الکترونیکی رشتۀ مهندسی کامپیوتر دانشگاه صنعتی خواجه نصیرالدین طوسی. ششمین کنفرانس ملی و سومین کنفرانس بینالمللی یادگیری و آموزش الکترونیک، تهران، مرکز آموزشهای الکترونیک دانشگاه تهران.
4
زﻧﺪی، ﺳﺎﺳﺎن؛ ﻋﺎﺑﺪی، دارﻳﻮش؛ ﻛﺒﻴﺮی، ﻋﻠﻲ؛ ﻳﻮﺳﻔﻲ، رﺿﺎ؛ ﭼﻨﮕﻴﺰ، ﻃﺎﻫﺮه؛ ﻳﻤﺎﻧﻲ، ﻧﻴﻜﻮ (1383). آموزش الکترونیکی به عنوان فناوری جدید آموزشی و ادغام آن در برنامۀ پژشکیهای آموزش. مجلۀ ایرانی آموزش در علوم پزشکی، (11)، 72-61.
5
عبداللهی، سید مجید؛ قدیری، صدیقه؛ تبریزیان، مریم (1393). بررسی تأثیر شاخصهای کیفی سامانۀ مدیریت یادگیری الکترونیکی دانشکدۀ آموزش مجازی دانشگاه اصفهان بر میزان کاربردپذیری سامانه توسط کاربران در سال 1393. مجموعه مقالات نهمین کنفرانس سالانۀ یادگیری الکترونیکی. تهران، دانشگاه خوارزمی با همکاری انجمن یادگیری الکترونیکی ایران (یادا)، 20-21 اسفند 1393.
6
قربانی، محسن (1390). بررسی سبکهای یادگیری همزمان و غیرهمزمان در یادگیری الکترونیکی. مجموعه مقالات اولین همایش ملی علمی ـ کاربردی فناوری اطلاعات و ارتباطات. دانشگاه جامع علمی ـ کاربردی مرکز ابهر، آذر90.
7
منتظر، غلامعلی؛ دیانی، محمد حسین (1382). دانشگاه مجازی. فصلنامۀ کتابداری و اطلاعرسانی، 6 (1)، 10-1.
8
یزدانی کاشانی، زینب؛ تمناییفر، محمدرضا (1392). اهمیت و جایگاه ابزارهای وب 2 در آموزش مجازی ـ پیادهسازی رویکرد تعاملی در دانشگاههای مجازی ایران. ماهنامۀ علمی ـ پژوهشی راهبردهای آموزش در علوم پزشکی، 6 (2)، 128-119.
9
References
10
Abdullahi, S.M., Ghadiri, S.,Tabrizian, M. (2015). Assessing the influence of qualitative indexes on user applicability for Esfahan University’s E-learning department LMS. Published at the 9th annual e-learning conference. Kharazmi University in cooperation with Iran’s E-learning Association, Tehran. (in Persian)
11
Ahmadi, H., Ahmadi, Sh., Biun, K. (2015). Assessing the effects of e-learning on the creativity of middle school grade 2 students in Aslanduz (Ardabil Province). Published at the 9th annual conference of e-learning. Kharazmi University in cooperation with Iran’s E-learning Association, Tehran.
12
(in Persian)
13
Atashak, M. (2007). Theoretical and applied principles of electronic learning. Quarterly Journal of Research and Planning in Higher Education, 13 (1), 135-156. (in Persian)
14
Feizi, K., Rahmani, M. (2004). Electronic learning in Iran problems and solutions: With emphasis on higher education. Quarterly journal of Research and planning in Higher Education, 10(3), 99-120.
15
Ghorbani, M. (2010). Evaluation of synchronous and asynchronous learning styles in e-learning. Published in the Proceedings of the First National Conference on Information and Communication Technology Applied Science. Applied Science University of Abhar. (in Persian)
16
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17
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18
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19
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20
Shih, H. F., Chen, S. H. E., Chen, S. C., & Wey, S. C. (2013). The Relationship among Tertiary Level EFL Students’ Personality, Online Learning Motivation and Online Learning Satisfaction. Procedia-Social and Behavioral Sciences, 103, 1152-1160.
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22
Yazdani Kashani, Z., Tamannayifar, M.R. (2013). Importance and Status of Web 2 Tools in Virtual Education; Implementing an Interactive Approach at Virtual Universities of Iran. Quarterly of Education Strategies in Medical Sciences, 6(2), 119-128.
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24
Zareyi Zevaraki, E. (2010). Measuring and evaluating Elearning: case report of E-Learning Course of Computer Engineering field. K. N. Toosi University of Technology. 6th National Conference and 3rd International Conference on Electronic Learning and Learning, Tehran University of Technology Electronic Education Center. (in Persian)
25
ORIGINAL_ARTICLE
What Kind of Knowledge Is Concealed by Project Team Members? (Case Study: Oil Industries’ Commissioning and Operation Company (OICO))
Knowledge hiding is one of the new concepts in the management of organizational knowledge. Although the nature of the relationship between projects teams members will have a substantial impact on the knowledge hiding behavior, different characteristics of Knowledge can also affect knowledge hiding. Based on the behaviors of the project teams, the aim of this research is to identify what characteristics of transferred knowledge between team members will lead to hiding or sharing it. The research model consisted of 4 variables: knowledge complexity, knowledge uniqueness, knowledge relatedness and knowledge sharing cost determined by the review of the literature and consultation with experts in management. The project team members were randomly selected and the data were gathered through a questionnaire. The results of the factor analysis and regression analysis showed that the uniqueness of knowledge had the greatest impact on knowledge hiding.
https://jitm.ut.ac.ir/article_63315_867665bdb4ee444fab5d7cd086e803c1.pdf
2018-06-01
355
374
10.22059/jitm.2017.211876.1756
Knowledge hiding
Project team
Task related knowledge
The complexity of knowledge
The cost of knowledge transfer
The uniqueness of knowledge
Malihe
Kamareiy
malihe.kamareiy@modares.ac.ir
1
MSc. Student in IT Management, Faculty of Management, University of Tarbiat Modares, Tehran, Iran
AUTHOR
A Reza
Hasanzadeh
hasanzadeh.alireza@gmail.com
2
Associate Prof. of Management, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
Shaban
Elahii
elahi@modares.ac.ir
3
Associate Prof. of Management, Tarbiat Modares University, Tehran, Iran
AUTHOR
پیام عسگری، محبوبه؛ هورشاد، هومن (1390). شناسایی عوامل موفقیت مدیریت دانش در شرکتهای پروژهمحور عمرانی. سومین همایش ملی ارتقای توان داخلی با رویکرد رفع موانع تولید در شرایط تحریم. 6 دی 1390، تهران: مرکز مطالعات تکنولوژی دانشگاه صنعتی شریف.
1
جعفری، سید محمد باقر؛ آرینفر، مسعود؛ الوانی، سید مهدی (1395). بررسی تأثیر انگیزههای فردی بر رفتار به اشتراکگذاری دانش. مدیریت فناوری اطلاعات، 8(2)، 272-253.
2
چهارسوقی، سید کمال؛ حسنی، مجید (1392). شناسایی، طبقهبندی و اولویتبندی ابزارهای اشتراکگذاری دانش در عرصۀ مدیریت پروژه. مدیریت فناوری اطلاعات، 5(3)، 62-43.
3
کشاورز، راحله (1392). بررسی اثر هنجارهای اجتماعی بر پنهانسازی دانش در سازمان. پایاننامۀ کارشناسی ارشد، تهران: دانشکدۀ مدیریت دانشگاه تهران.
4
لبافی، سمیه؛ قلیپور، آرین (1394). ارائۀ مدل زمینهای پنهانسازی دانش در شرکتهای تولید نرمافزار. پژوهشهای مدیریت منابع سازمانی، 5(1)، 25-1.
5
ناصری، علیرضا (1392). پنهان کردن دانش سازمانی؛ بررسی عوامل مؤثر بر پنهان کردن دانش در سطح فردی. پایاننامۀ کارشناسی، تهران: دانشکدۀ مدیریت دانشگاه آزاد اسلامی واحد تهران مرکزی.
6
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39
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ORIGINAL_ARTICLE
Presenting a Text Mining Algorithm to Identify Emotion in Persian Corpus
The literature regarding Persian text mining indicates that most studies are conducted to detect polarity of opinions on social websites. The aim of this research is presenting an algorithm to identify emotion implemented in the text based on the following six main emotions of happiness, sadness, fear, anger, surprise and disgust. In this research, the emotion will be examined based on unsupervised lexicon method. Identifying emotions conveyed by the texts based on a single emotional word will produce low accuracy because the intervening boosters and negating words can influence the emotion of the text too. Therefore, the algorithm has been implemented in six approaches with different features. In the first approach, the algorithm is capable of detecting only one emotional word in a sentence, and then it improves to detect boosters and negating and stop word list as well. The results of running the algorithm on two domains of data showed that the more features used in the algorithm, the more accurate the algorithm becomes and that the most effective factor is part of speech.
https://jitm.ut.ac.ir/article_63112_b55bc91774ae4a15d3dd92b8c143146c.pdf
2018-06-01
375
389
10.22059/jitm.2017.224726.1923
Data Mining
Emotion analysis
Sentiment mining
Text mining
Web mining
Masoud
Garshasbi
m_garshasbi@itrc.ir
1
Research Instructor, Faculty of Iran Telecommunication Research Center, Tehran, Iran
AUTHOR
Anahid
Rais-Rohani
anahidrr@gmail.com
2
MSc, Software Engineering, Islamic Azad University, Karaj Branch, Tehran, Iran
LEAD_AUTHOR
Mohammadreza
Kabaranzadeh Ghadim
kabaranzad@yahoo.com
3
Associate Prof. of Management, Islamic Azad University, Central Tehran Branch, Tehran, Iran
AUTHOR
علیمردانی، سعیده و آقایی، عبداله (2015). اندیشهکاوی در زبان فارسی. فصلنامۀ مدیریت فناوری اطلاعات، 2(7)، 362- 345.
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