Sentimental Content Analysis and Prediction of Text

Abstract

In the advancement of technology, the web era revolutionized mankind life; huge amounts of data are available on the internet in the form of articles and blogs. From this huge volume of data opinion mining is an important for extracting the raw data to become useful information. Sentiment analysis provides categorization in opinion mining as positive or negative class for content analysis. English language is considered as a universal language and used almost every part of the word, so classification of opinion is important to get the end meaning of the word phrase and comments. No literature is available for classification of sub opinion in the text mining. SAP of Text through Machine Learning algorithm (KNN) is a three-step technique of opinion mining. In this study, authors have put articles at first removing stop-words, tokenizing the sentence and revamping the tokens, it will calculate the polarity of the sentence, paragraph and text through contributing weighted words by keeping sentiment shifters and intensity clauses in consideration. Secondly, over polarization of sentence is adjusted. Finally, overall trend of the input text on the basis of tokenization and polarization of sentence is predicted with proposed algorithm and compared with KNN. Furthermore, domain specific analysis is a distinct feature of the proposed model where data can be updated according to the required domain to ensure the optimal level of efficiency.