Implementation of Sentiment Analysis Using Optimized Classifier

• Author(s): Shahrukh Ansari ; Devansh Arora ; Abhishek Kumar ; Neetu Garg
• Paper ID: 1704196
• Page: 276-278
• Published Date: 29-03-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 9 March-2023

Abstract

Emotion recognition (ERC) in speech is a challenging task that has recently gained popularity for its potential applications. Words and phrases reflect people’s views on products, services, governments, and events on social media. The main aim of sentiment analysis is to categorize the emotion behind a text as positive or negative. There is significant upsurge in adopting sentiment analysis techniques in various business organizations and government projects as it helps analyze the audience’s emotion towards a particular aspect. Using the knowledge obtained through sentiment analysis can prove to be beneficial for organizations in terms of sales and popularity. Naive Bayesian classifiers greatly simplify mastery by assuming that skills are independent of a particular class. We study the effect of distributional entropy on type error and show that low-entropy feature distributions provide accurate overall performance of naive Bayes. We also show that Naive Bayes works correctly for positive near-functional functional dependencies. So, it achieves overall good performance and otherwise perfectly fair functionality (as expected) and functionally structured functionality (which is unexpected). Another unexpected result is that the accuracy of Naive Bayes does not always immediately correlate with the level of functional dependence. We plan to improve this.

Citations

IRE Journals:
Shahrukh Ansari , Devansh Arora , Abhishek Kumar , Neetu Garg "Implementation of Sentiment Analysis Using Optimized Classifier" Iconic Research And Engineering Journals Volume 6 Issue 9 2023 Page 276-278

IEEE:
Shahrukh Ansari , Devansh Arora , Abhishek Kumar , Neetu Garg "Implementation of Sentiment Analysis Using Optimized Classifier" Iconic Research And Engineering Journals, 6(9)