Ensemble of Weighted Code Mixed Feature Engineering and Machine Learning-Based Multiclass Classification for Enhanced Opinion Mining on Unstructured Data

There is an exponential growth of opinions on online platforms, and the rapid rise in communication technologies generates a significant need to analyze opinions in online social networks (OSN). However, these opinions are unstructured, rendering knowledge extraction from opinions complex and challenging to implement. Although existing opinions mining systems are applied in several applications, limited research is available to handle code-mixed opinions of a non-structured nature where there is a switching of lexicons in languages within a single opinion structure. The challenge lies in interpreting complex opinions in multimedia networks owing to their unstructured nature, volume, and lexical structure. This paper presents a novel ensemble approach using machine learning and natural language processing to interpret code mixed opinions efficiently. Firstly, the opinions are extracted from the input corpus and preprocessed using proposed Extended Feature Vectors (EFV). Subsequently, the opinion mining system is implemented using a novel approach using weighted code mixed opinion mining framework (WCM-OMF) for multiclass classification. The proposed WCM-OMF model achieves an accuracy of 79.11% and 72% for the benchmark datasets, which is a significant improvement over existing Hierarchical LSTM, Random Forest, and SVM models and state-of-the-art-methods. The proposed solution can be implemented in opinion detection of other business sectors beneficial in obtaining actionable insights for efficient decision-making in enterprises and Business Intelligence (BI).

@article{Sharma2024,
  title     = {Ensemble of Weighted Code Mixed Feature Engineering and Machine Learning-Based Multiclass Classification for Enhanced Opinion Mining on Unstructured Data},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Ruchi Sharma and Pravin Shrinath},
  doi       = {10.14569/IJACSA.2024.01510124},
  url       = {https://doi.org/10.14569/IJACSA.2024.01510124}
}