"A Hybrid Approach to Sentiment Analysis: Combining Rule-Based and Machine Learning Techniques"
DOI:
https://doi.org/10.48047/8eh7sz95Keywords:
SVM, Hybrid Classification, Sentiment analysis, ML, MLTAbstract
The Sentimental Analysis method is frequently utilized to assess user thoughts, sentiments, and text subjectivity. Sentiment Analysis, also known as Opinion Mining, entails the thorough evaluation of emotions conveyed by individuals. The websites function as an effective medium for collecting client feedback derived from historical data. The existing methods employing sentiment analysis have demonstrated ineffectiveness. A new hybrid framework has been established, integrating three classifiers: SVM, logistic regression, and Random Forest. The hybrid model functions as a proficient classifier that improves classification results by user
feedback or historical data. The proposed model has been effectively applied and assessed against current methodologies utilizing several performance criteria, including as accuracy, precision, and recall.
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References
Woldemariam, Y. (2016) ‘Sentiment analysis in a cross-media analysis framework’. IEEE International Conference on Big Data Analysis (ICBDA).
Fan, X., Li, X., Du, F., Li, X. and Wei, M. (2016) ‘Apply word vectors for sentiment analysis of APP reviews’. 3rd International Conference on Systems and Informatics (ICSAI).
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