Design novel algorithm for sentimental data classification based on hybrid machine learning
Jyoti Srivastava, Neha Chiruvolu Singh · 2023
Natural language processing can be used to identify emotional content in text or spoken utterances in general. One example of this type of processing is sentimental analysis. Online monitoring and listening tools analyse and characterise emotions in various ways, and each has a varied level of performance and accuracy in doing so. Market feedback can be used to provide quantitative service measurement and sentiment analysis of teacher reviews. The qualitative measurement of accuracy, on the other hand, is complex since it requires feature extraction and machine learning methods for example support vector machines (SVM) and Naive Bayes classifier. Correct analysis and interpretation of sensations because sentiment analysis is far more sophisticated, the SVM classifier outperforms the Naive Bayes classifier in terms of accuracy and speed. We will look at how to classify the sentiment of a teacher review dataset in this paper. Create a new technique to identify sentimental data using a fusion-based algorithm.