چكيده به لاتين
With the expansion of social networks among people all over the world, as well as the increase of textual data available in social networks, one of the main concerns of organizations is the correct use of this data so that they can in short find out the real feelings of their users as soon as possible and with the fastest method, and also be able to monitor the change of their users' feelings with the help of various tools in order to offer their products and services at the best possible time and to be successful and or the failure of the product from the users' point of view, since the analysis of the text data that is being published is a specialized and difficult task, therefore, in this method, relying on text processing techniques and emotion identification Users first get a correct understanding of their real feelings towards a subject, and then we analyze the behavior of users to obtain an accurate behavioral pattern of each user, and according to this pattern, whenever users experience sudden changes in feelings to identify it and find a correct insight into the fluctuations of users' feelings, and then identify different communities based on the same hashtags regarding the issues raised by them and the reasons for their satisfaction and dissatisfaction according to keywords I discovered my hatred. In the past, various techniques have been used to detect the emotions of users, which have a high diversity, and their emotional changes have been studied in different periods, but the detection of anomalies in the level of the user's and society's emotions is also Changes in emotions is an important work that has not been paid much attention to. In this method, by using traditional methods of the past to analyze sentiments and use anomaly detection at the user and community level, as well as finding different communities based on the same hashtags and discovering repeated words in those communities. It has been tried to get a better and more accurate understanding of emotions, change of emotions and reasons for change of emotions in social networks.
