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A Novel Approach for Extraction of Distinguish Emotions for Semantic Granularity Level Sentiment Analysis in Multilingual Context



 Extraction of distinguishing semantic level emotions posed in multi-languages over social media
is an essential task in the field of sentiment analysis or opinion mining. The extraction of emotions expressed in Dravidian
or local languages combining with multi-languages over social media has become an essential challenge in the field of big
data sentiment analysis.



 In the proposed approach, an innovative framework to recognize the sentiments of users in multi-languages or
Dravidian languages text data using scientific linguistic theories has been defined. The proposed method used machine
learning techniques such as naïve Bayes, support vector machine for fine-grained classification of multilingual text with
help of lexicon-based features groups.



The results obtained by the experiments conducted on collected benchmark datasets in the proposed approach are
outperformed and better in comparison with corpus-based and world level, phrase-level sentiment analysis for multilanguages text.



 Machine learning technnique SVM has outperformed for sentiment and emotion extraction.

