درخت حوزه‌های تخصصی

Internet-based social channels have turned into an important information repository for many people to get an idea about current trends and events happening around the world. As a result of Abundance of raw information on these social media platforms, it has become a crucial platform for businesses and individuals to make decisions based on social media analytics. The ever-expanding volume of online data available on the global network necessitates the use of specialized techniques and methods to effectively analyse and utilize this vast amount of information. This study's objective is to comprehend the textual information at the Lexical and Semantic level and to extract sentiments from this information in the most accurate way possible. To achieve this, the paper proposes to cluster semantically related words by evaluating their lexical similarity with respect to feature and sequence vectors. The proposed method utilizes Natural Language Processing, semantic and lexical clustering and hybrid C4.5 algorithm to extract six subcategories of emotions over three classes of sentiments based on word-based analysis of text. The proposed approach has yielded superior results with seven existing approaches in terms of parametric values, with an accuracy of 0.96, precision of 0.92, sensitivity of 0.94, and an f1-score of 0.92.

Purpose: Technology plays an essential role in any country to achieve sustainable development. Choosing appropriate and advanced technology is one of the strategic decisions of industrial managers. This research discusses the critical factors that should be considered in the process of choosing new technologies based on the competitive mechanism, and accordingly, designs and explains the technology selection model for equipping downstream petrochemical industrial units to create a competitive advantage.Method: The research method is an applied one and in terms of data type, it is a partly qualitative and quantitative research. In the quantitative phase, research data was collected based on the data obtained from the qualitative phase, and using the questionnaire tool. The research data was analyzed using thematic analysis and MaxQda software in the qualitative phase and they were also  analyzed using the TOPSIS technique and meta-heuristic algorithm in the quantitative phase for modeling.Findings: Thirty-four concepts and five categories were identified as technology selection criteria for equipping downstream petrochemical industrial units to create a competitive advantage. The "technical criteria" factor was identified as the foremost priority in equipping downstream petrochemical industrial units to create a competitive advantage. The ranking of the determined categories from one to five were the factors of competitive criteria, strategic criteria, internal criteria, and external criteria, respectively.Conclusion: Finally, using the TOPSIS fuzzy technique, the model of equipping industrial units was presented to help decision-makers and policy makers of downstream petrochemical industries.

Providing credit facilities to clients can be regarded as one of the most important tasks of banks. As a result, the current research is carried out with the aim of developing a model of financial and non-financial factors that influence credit risk in the Maskan Bank. This study was divided into two sections: qualitative and quantitative. In the qualitative portion, the Delphi technique was used to identify financial and non-financial variables impacting credit risk in the Maskan Bank by interviewing 18 experts. In the quantitative section, the selected components of the questionnaire were initially created in a 5-point Likert scale. The questionnaire was then presented to 361 legal clients of Maskan Bank from the statistical sam-ple of the quantitative section. The quantitative component was completed using LISREL software and modeling the structural equations of SEM. Three primary components (financial, market, and management) and 33 sub-components were identified as financial and non-financial indicators impacting credit risk based on the results of the qualitative section. The quantitative findings also revealed that the indicators of guarantee status in the financial component, price level in the market component, and manpower motivation and productivity, as well as man-agement continuity and trade in the management component, had the largest factor loads.