Fateme Nayeblui; Mostafa Assi; Azita Afrashi; Masood Ghayoomi
Abstract
Vocabulary differences between languages indicate differences in the cultures associated with each language. Polysemous words in any culture can be an expression of such lexical differences in a language. In cognitive semantics, there are different approaches to the polysemous phenomenon in language; ...
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Vocabulary differences between languages indicate differences in the cultures associated with each language. Polysemous words in any culture can be an expression of such lexical differences in a language. In cognitive semantics, there are different approaches to the polysemous phenomenon in language; The theory of Frame semantics is one of these approaches in cognitive semantics that has looked at the issue of polysemy from the perspective of semantic frames. The revised form of this theory has been realized in FrameNet . Semantic frames are semantic packages and each frame is composed of semantic components called frame elements. Semantic relationships in this network are presented as relationships between semantic frames. In this article, an attempt is made to provide a semantic and corpus-based analysis of the polysemy of the conceptual domain of the verb "Listening" in Persian based on the principles of the FrameNet network in English. Accordingly, the keywords "šeno", " šenid" and "guš" have been studied in the Persian database . Among the concepts found in sentences extracted from the corpus, six semantic frames of Perception_active, Perception_experience, Awareness, Compliance, Seeking and Attention and two "inheritance" Frame to frame relations between "Perception-Active" and "Perception-Experience" Frames and "use" Frame to frame relations between "Attention" and Perception-Active" Frames were extracted. The results of the analysis show that the current approach to the phenomenon of polysemy is an efficient approach to study the meaning of the verb "Listening" in Persian and can provide a picture of Persian language culture in this conceptual area.
Masood Ghayoomi
Abstract
Coronavirus pandemic caused changes in the daily lifestyle, such as reducing social interactions and creating social distancing. In this research, we pursue two goals. One is algorithmic content analysis of comments/posts in Persian related to the Coronavirus on two social media, namely Tweeter and Instagram. ...
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Coronavirus pandemic caused changes in the daily lifestyle, such as reducing social interactions and creating social distancing. In this research, we pursue two goals. One is algorithmic content analysis of comments/posts in Persian related to the Coronavirus on two social media, namely Tweeter and Instagram. To this end, topic modeling is used as a method for content analysis to cluster the data into abstract topics. The other goal is finding the correlation between topics and hashtags in the comments/posts. To this end, we developed a corpus from these two social media. We found 24 abstract topics by algorithmic content analysis of this corpus and they were manually labeled to be comprehensive. According to the corpus and the statistical information of the extracted topics, it can be speculated that about 25% of the comments/posts in this corpus focused on political and social issues of the virus. 10 fine-grained topics which contained 35% of the comments were related to the Coronavirus itself and its pandemic property. This indicates the importance of the attention that has been paid to social media for informing and disseminating information. Furthermore, the hypothesis of existing correlation between topics and hashtags was studied from statistical point of view by using the Pearson correlation coefficient. For 20 topics, a high correlation score between topics and hashtags was found; but this correlation was not found for 4 topics. The outcome of this research can be used to increase the internal coherence of a text and to make the hashtags predictable.
