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

Introduction: Extraction of distinguishing semantic level emotions posed in multilanguages 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 multilanguages over social media has become an essential challenge in the field of big data sentiment analysis. Methods: 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 the help of lexicon-based features groups. Results: 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 multi-languages text. Conclusion: Machine learning technique SVM has outperformed for sentiment and emotion extraction.