An Advanced Document Representation Technique Based Approach for Author Profiles Prediction using Word Embedding Techniques
Keywords:
Word Embedding Techniques, Term Weight Measures, deep larning, Gender Prediction, Age PredictionAbstract
The internet has become exponentially larger and unmanageably fast, the main contributing factor can be attributed due to how many people utilise social media, blogs, as well as online reviews. The most of the information given was published in various settings by multiple writers. The abundance of the information challenged Academics & information analysts collaborate to develop automatic methodologies for evaluating such content. Author Profiling is a widely employed approach in scholarly research, wherein scholars analyse the writing styles of authors to extract maximum information from texts. Author profiling is a methodology employed in the field of text categorization used to identify writers by their written works and forecast their demographic attributes, such as gender, age, native language, schooling, location, as well as personality traits. In today's information age, author profiling is a crucial approach with applications in forensic investigation, security, and marketing. Social media platforms have a substantial influence on our daily existence. and are a source of crimes, including public humiliation, fraudulent profiles, defamation, blackmail, stalking, etc. Author profiling helps the educational field by examining a big group of students. It aids in exposing the pupils' extraordinary potential. The educational forum also aids in determining the optimal amount of knowledge for individual students or groups of students. The majority of individuals of author profiling techniques employed a variety of Various criteria, encompassing linguistic factors, Various writing styles can be told apart by their content-based features, structure features, syntactic features, as well as semantic features. The present ones and models did there is no evidence to suggest that the enhancement of profile prediction accuracy has been achieved. They utilised new methods to improve the accuracy of demographic predictions for word embedding that are rooted in document analysis representation technique, it offers a new collection of style characteristics, feature selection algorithms, word weight measures, and the gender & age prediction models achieved accuracies of 0.9439 and 0.8945, respectively. The present study employs the PAN Competition 2014 evaluations database to do gender & age prediction. The experimental results are outperforming the earlier models and superior in estimation level.
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