Open AccessA Comparison of Machine Learning and Deep Learning Methods with Rule Based Features for Mixed Emotion Analysis
Author(s) -
Christy Daniel,
S. Loganathan
Publication year - 2021
Publication title -
international journal of intelligent engineering and systems
Language(s) - English
Resource type - Journals
eISSN - 2185-310X
pISSN - 1882-708X
DOI - 10.22266/ijies2021.0228.05
Subject(s) - computer science , adjective , artificial intelligence , noun , natural language processing , sentiment analysis , emotion detection , support vector machine , task (project management) , class (philosophy) , convolutional neural network , machine learning , emotion recognition , management , economics
Multi-class classification of sentiments from text data still remains a challenging task to detect the sentiments hidden behind the sentences because of the probable existence of multiple meanings for some of the texts in the dataset. To overcome this, the proposed rule based modified Convolutional neural network-Global Vectors (RCNN-GloVe) and rule-based modified Support Vector Machine - Global Vectors (RSVM-GloVe) were developed for classifying the twitter complex sentences at twelve different levels focusing on mixed emotions by targeting abstract nouns and adjective emotion words. To execute this, three proposed algorithms were developed such as the optimized abstract noun algorithm (OABNA) to identify the abstract noun emotion words, optimized complex sentences algorithm (OCSA) to extract all the complex sentences in a tweet precisely and adjective searching algorithm (ADJSA) to retrieve all the sentences with adjectives. The results of this study indicates that our proposed RCNNGloVe method used in the sentiment analysis was able to classify the mixed emotions accurately from the twitter dataset with the highest accuracy level of 92.02% in abstract nouns and 88.93% in adjectives. It is distinctly evident from the research that the proposed deep learning model (RCNN-GloVe) had an edge over the machine learning model (RSVM-GloVe).