Paper Title
LEGITIMATE NEWS DETECTION IN EURASIAN LANGUAGES WITH LOW RESOURCES

Abstract
Natural Language Processing (NLP) has recently focused on an important study topic called detection and recognition from text, which may provide some insightful information for a number of applications. It can be applied to a variety of domains, including psychology, human-computer interaction, data mining, online learning, and recommendation systems. The content of these brief texts can be a great resource for text mining to find and unhide numerous characteristics, including. Writings today take on many different formats, including social media posts, microblogs, news articles, consumer reviews, etc. The experimental findings will demonstrate that the suggested model successfully recognizes a variety of states and can further be enhanced by additional word embedding’s. The various visible ways that is expressed include voice, written language, gestures, and facial expressions. Text document detection is essentially a content-based classification issue that incorporates ideas from both the machine learning and natural language processing fields. Using common datasets, we thoroughly evaluated the suggested model's performance. Text document detection is essentially a content-based classification issue that incorporates ideas from both the machine learning and natural language processing fields. Keywords - Natural Language Processing, Text Classification, Machine Learning, Deep Neural Networks