جستجوی مقالات مرتبط با کلیدواژه « مدل سازی موضوع » در نشریات گروه « ادبیات و زبان ها »

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.

Since the advent of the Internet, we have come across a large amount of data that has been placed on this platform. This has caused the way data is studied and examined to evolve from manual to automatic. This research aims to extract the topics raised in articles in the field of language and linguistics and to study the evolution of topics over time with the help of the topic modeling algorithm. To this end, the Latent Dirichlet allocation algorithm is used. The corpus of this research was developed through crawling; and after filtering and pre-processing of the data, the number of 5, 10 and 15 topics are extracted from the articles; and based on the words of each topic, they are manually labeled. Due to the distribution of articles from 1927 to 2020 (1306 to 1399 according to the solar calendar), the time is divided into 5 time span and the topics related to each time span is identified after the labeling process. Among the 5 extracted topics, including Education, Discourse Analysis, Psychology, Syntax and Dialectology, all topics, except Discourse Analysis, are progressively studied over time. The growth and decline of the topics extracted from the articles can be seen in 10 and 15 topics. The practical achievement of this research is policy-making in the field of science. In addition to introducing a practical, technological methodology in do research, it is possible to identify hot topics among researchers in a scientific field and to find the gaps in research topics and to try to diversify and balance research topics.