فهرست مطالب

Web Resrarch - Volume:6 Issue: 1, Spring-Summer 2023

International Journal of Web Research
Volume:6 Issue: 1, Spring-Summer 2023

  • تاریخ انتشار: 1402/03/21
  • تعداد عناوین: 4
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  • Maryam Tayefeh Mahmoudi *, Amirmansour Yadegari, Parvin Ahmadi, Kambiz Badie Pages 1-8
    Content interpretation is a cognitive ability which is mostly concerned with understanding the intention of the person who has created or narrated the content.  Narrator is also important since his/ her specific intention, which is inevitable in media like a social network, may change the reality of a created content. Here, the focal point is a sort of transformation with the aim of yielding the class of a message behind the content. In this paper, we propose a rule-based framework for interpreting contents in a social network that has the ability to perform such a transformation through using some generic rules with propositions at high abstraction level. The reason for selecting abstract propositions is their ability in covering wide range of facts occurring in the real world situation. Our suggested framework is in reality able to determine the class of a message indicating the possible intention of either a content’s creator or its narrator, such as whether a narrator is seeking honesty/justice toward the others, is after respect for the people, cares for compassion/ mercy, emphasizes a significance of knowing/ thinking in life, or is after self-upgradation to conduct a healthy life. These classes of message are determined according to both philosophical and psychological aspects which do exist behind the cognitive, emotional and ethical faculties in human being. Results of some experiments show that the generic rules proposed in this paper, which are structured on the ground of abstract propositions, have enough ability to respond successfully to the issue of interpretation in a social network with the characteristics already mentioned. Also, these results approve the fact that such an abstraction is able enough to handle the possible facts hidden in the contents showing up in social network.
    Keywords: social network, content, interpretation, Generic Rule, Abstract Proposition
  • Soroush Elyasi *, Arya Varaste Nezhad, Fattaneh Taghiyareh Pages 9-16
    Apprehending the personality types of software engineers is essential for both individuals and organizations, especially in software engineering, which heavily relies on teamwork and soft skills. This paper explores various aspects and dimensions of personality exhibited by software engineers, focusing on Iranian culture. To achieve this, we conducted a comprehensive study that involved analyzing existing research on a global scale and a case study specifically targeting professional software engineers in Iran. The Myers-Briggs Type Indicator test was utilized to gather data, and the responses were carefully filtered, resulting in 102 valid datasets for analysis, representing both the private and public sectors. Our methodology included the development of a comprehensive questionnaire comprised of personal and standardized MBTI questions in Persian. The findings of our study indicate that software engineers in Iran predominantly exhibit a thinking personality rather than a feeling one. Moreover, personality types such as ISTJ, INTJ, ESTJ, and ENTJ were observed to be more prevalent among software engineers, while ISFJ, ISFP, ESFP, ENFP, and ESFJ were less common. While these results align with global trends, there are also noteworthy distinctions among Iranian software engineers. The implications of our research extend to practical applications for managers, human resources specialists, and recruiters. By understanding software engineers' personality types and traits, employers can optimize talent acquisition strategies, improve job placements, and tailor career development programs accordingly. This knowledge is helpful for students and those interested in a career in software engineering. It aids in making informed decisions that meet the field's requirements.
    Keywords: Behavioral Data Analysis, Human Factors in Software Engineering, Empirical Software Engineering, human resources, MBTI
  • Mohammadreza Samadzadeh *, Najmeh Farajipour Ghohroud Pages 17-28
    Accurate traffic classification is important for various network activities such as accurate network management and proper resource utilization. Port-based approaches, deep packet inspection, and machine learning are widely used techniques for classifying and analyzing network traffic flows. Most classification methods are suitable for small-scale datasets and cannot achieve a high classification accuracy owing to their shallow learning structure and limited learning ability. The emergence of deep learning technology and software-driven networks has enabled the application of classification methods for processing large-scale data.In this study, a two-step classification method based on deep learning algorithms is presented, which can achieve high classification accuracy without manually selecting and extracting features. In the proposed method, an Autoencoder was used to extract features and remove unnecessary and redundant features. In the second step, the proposed method uses the features extracted by the autoencoder from a hybrid deep-learning model based on the CNN and LSTM algorithms to classify network traffic.To evaluate the proposed method, the results of the proposed two-stage hybrid method is compared with comparative algorithms including decision tree, Naïve Bayes, random forest. The proposed combined CNN+LSTM method obtains the best results by obtaining values of 0.997, 0.972, 0.959, and 0.964, respectively, for the evaluation criteria of, accuracy, precision, recall, and F1 score.The proposed method is a practical and operational method with high accuracy, which can be applied in the real world and used in the detection of security anomalies in networks using traffic classification and network data.
    Keywords: Network Traffic Classification, Deep Learning, Software-oriented Network, Autoencoder
  • Mohammad Dehghani *, Zahra Yazdanparast Pages 29-37
    Sentiment analysis is the process of identifying and categorizing people’s emotions or opinions regarding various topics. The analysis of Twitter sentiment has become an increasingly popular topic in recent years. In this paper, we present several machine learning and a deep learning model to analysis sentiment of Persian political tweets. Our analysis was conducted using Bag of Words and ParsBERT for word representation. We applied Gaussian Naive Bayes, Gradient Boosting, Logistic Regression, Decision Trees, Random Forests, as well as a combination of CNN and LSTM to classify the polarities of tweets. The results of this study indicate that deep learning with ParsBERT embedding performs better than machine learning. The CNN-LSTM model had the highest classification accuracy with 89 percent on the first dataset and 71 percent on the second dataset. Due to the complexity of Persian, it was a difficult task to achieve this level of efficiency. The main objective of our research was to reduce the training time while maintaining the model's performance. As a result, several adjustments were made to the model architecture and parameters. In addition to achieving the objective, the performance was slightly improved as well.
    Keywords: Sentiment analysis, Persian, Machine Learning, Deep Learning, Twitter