درخت حوزه‌های تخصصی

This research examines the Iranian digital community's engagement with the Metaverse, analyzing 14,724 tweets (expanding to 48,620 with retweets) from 23,571 unique users. Interest in the Metaverse among Iranians surged in the seven months following Facebook's rebranding but has since waned. A significant link between digital currency discussions and Metaverse topics was observed. Economic activists in the cryptocurrency market, forming the primary content creators, generally portray the Metaverse positively, focusing on earning opportunities. Another active group includes technology enthusiasts who share the latest Metaverse developments. Moreover, some users integrate Iran's socio-political context into their discussions, often contrasting Iran's technological stance with global giants like Facebook and addressing internet censorship. A distinct perspective comes from users wary of potential colonialist uses of technologies like the Metaverse, expressing apprehension about human subjugation. This sentiment forms the basis of most negative views on the Metaverse's development within the Iranian digital sphere.

« Postmoderne », « hypermoderne » ou encore « extrême contemporaine » sont généralement les adjectifs attribués à la littérature depuis les années 80 du XXème siècle. De telles appellations prônent une certaine allusion ironique à une « fin » et/ou à une « mort » non seulement de l’art, mais aussi de la littérature, qui part du passé, rejoue ses codes et annonce le recommencement. C’est une littérature de l’entre-deux qui cherche à rendre palpable une dynamique de renouvellement dans une visée de jeu, à portée ludique et parodique, tout en recommandant la construction à partir de la déconstruction.

With the advent of user-generated text information on the Internet, text sentiment analysis plays an essential role in online business transactions. The expression of feelings and opinions depends on the domains, which have different distributions. In addition, each of these domains or so-called product groups has its vocabulary and peculiarities that make analysis difficult. Therefore, different methods and approaches have been developed in this area. However, most of the analysis involved a single-domain and few studies on cross-domain mood classification using deep neural networks have been performed. The aim of this study was therefore to examine the accuracy and transferability of deep learning frameworks for the cross-domain sentiment analysis of customer ratings for different product groups as well as the cross-domain sentiment classification in five categories “very positive”, “positive”, “neutral”, “negative” and “very negative”. Labels were extracted and weighted using the Long Short-Term Memory (LSTM) Recurrent Neural Network. In this study, the RNN LSTM network was used to implement a deep transfer learning framework because of its significant results in sentiment analysis. In addition, two different methods of text representation, BOW and CBOW were used. Based on the results, using deep learning models and transferring weights from the source domain to the target domain can be effective in cross-domain sentiment analysis.