درخت حوزه‌های تخصصی

The present study was carried out with the purpose of examining the role of metaphorical language in the critical discourse analysis (CDA) of political texts based on a modern framework postulated by Kövecses (2015). The corpus of the study consisted of thirty-thousand words chosen as a textual sample to see which source conceptual domains are used and what generic/discursive attributes emerge upon the analysis. It was prepared through systematic random sampling from different editorial articles in Western political magazines on the relations between Iran and the West in the time span of 2010-2019. Then, Critical Metaphor Analysis was used to explain the trend and patterns in the political articles, mostly lending support to previous research by authority figures in critical and political metaphor analysis that metaphors are used in shaping political thinking, in aiding political persuasion, and in steering cognitive scenarios and models towards the shaping of interpretations of the nations as person or body metaphors. Finally, the study lent ample support to this latter view in its textual findings of the conceptual metaphors used. The findings of the study provide foreign language education in general and the EFL classrooms in particular, with some implications for further research.

Being aware of the waiting time for selling residential units is one of the important issues in the housing sector for the majority of people, especially investors. There are several factors affecting the waiting time for selling residential units. Determining the influential factors on the time period of selling real estates can lead to an informed decision making by real estate consultants, sellers as well as those seeking to buy real estates. Using a real estate database in Iran, the present paper proposes a two-module procedure. The first module deals with implementation of association rule mining. Using the well-known association rule mining techniques namely FP-Growth, several association rules have been extracted which indicate the effective factors on the waiting time for selling residential units. Generated association rules have been evaluated based on metrics such as support, confidence and lift and finally the best rules are selected. The main objective of the second module is to develop a fuzzy inference system which can determine the factors influencing the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments. In conclusion section, a number of suggestions for future studies are presented. For example, machine learning algorithms such as neural networks, decision trees, etc. can also be used to predict the duration of residential units’ sale. The main objective of the second module is to develop a fuzzy inference system which can learn about the factors that influence the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it takes to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments.