درخت حوزه‌های تخصصی

Sentiment Analysis or opinion mining is NLP's method to computationally identify and categorize user opinions expressed in textual data.  Mainly it is used to determine the user's opinions, emotions, appraisals, or judgments towards a specific event, topic, product, etc. is positive, negative, or neutral. In this approach, a huge amount of digital data generated online from blogs and social media websites is gathered and analyzed to discover the insights and help make business decisions. Social media is web-based applications that are designed and developed to allow people to share digital content in real-time quickly and efficiently.  Many people define social media as apps on their Smartphone or tablet, but the truth is, this communication tool started with computers. It became an essential and inseparable part of human life. Most business uses social media to market products, promote brands, and connect to current customers and foster new business. Online social media data is pervasive. It allows people to post their opinions and sentiments about products, events, and other people in the form of short text messages. For example, Twitter is an online social networking service where users post and interact with short messages, called "tweets." Hence, currently, social media has become a prospective source for businesses to discover people's sentiments and opinions about a particular event or product. This paper focuses on the development of a Multinomial Naïve Bayes Based social media data emotion analyzer and sentiment classifier. This paper also explains various enriched methods used in pre-processing techniques. This paper also focuses on various Machine Learning Techniques and steps to use the text classifier and different types of language models.

In an EPS lesson, the student can learn in three different ways, including learning by doing, observing, or thinking; the last method will be the subject of our study, the objective of which is to show how a debate of ideas (sociocognitive conflict), between a group of students will make it possible to construct learning through the educational mediation of the teacher? So by verbalizing the problems encountered in learning situations and concretizing theoretical behaviors (proceduralization of knowledge), students will be able to learn in an autonomous and responsible manner (self-management). This expected result is one of the purposes of competence-based pedagogy as well as the pedagogical orientations, namely to train a citizen capable of developing powers of analysis and reflection and the ability to adapt (the national charter for education and training) Control groups were planned to facilitate comparison of the results. At the end of the experiment, we noticed that the students in the experienced groups made greater progress than those in the control groups. The progress that has been made by the experienced groups confirms the research hypothesis that the learning built by the students themselves through a collective project based on the verbalization of actions and problem solving is not only more profitable for the students but also for the teacher