Efficient Analysis for Extracting Feature and Evaluation of Text Mining using Natural Language Processing Model

Text mining is the process of extracting the word from the document which is collected from various sources. It is used to transform the text into normalized data for performing machine learning operations. Feature extraction process in the text considers various words in the text then converted into features. Traditional extraction methods extract the features without semantic meaning of the data due to different data formats. Because of the natural language representation, the text mining process uses the document as an input and evaluates the meaning and relationship between the documents. The main objective of the proposed analysis for extracting the features forms the text with various models and parameters. Bag of Words, GloVe, Word2Vec, TF-IDF and Doc2Vec models are analyzed. This analysis considers parameters like cost, number of iteration, learning rate, similarity measure and object relationship for performance measurement. This model uses the Wikipedia data source for analysis and gives raw data with high volume