Evaluation of Sentiment Analysis based on AutoML and Traditional Approaches

AutoML or Automated Machine Learning is a set of tools to reduce or eliminate the necessary skills of a data scientist to build machine learning or deep learning models. Those tools are able to automatically discover the machine learning models and pipelines for the given dataset within very low interaction of the user. This concept was derived because developing a machine learning or deep learning model by applying the traditional machine learning methods is time-consuming and sometimes it is challenging for experts as well. Moreover, present AutoML tools are used in most of the areas such as image processing and sentiment analysis. In this research, the authors evaluate the implementation of a sentiment analysis classification model based on AutoML and Traditional approaches. For the evaluation, this research used both deep learning and machine learning approaches. To implement the sentiment analysis models HyperOpt SkLearn, TPot as AutoML libraries and, as the traditional method, Scikit learn libraries were used. Moreover for implementing the deep learning models Keras and Auto-Keras libraries used. In the implementation process, to build two binary classification and two multi-class classification models using the above-mentioned libraries. Thereafter evaluate the findings by each AutoML and Traditional approach. In this research, the authors were able to identify that building a machine learning or a deep learning model manually is better than using an AutoML approach.

@article{Mahima2021,
  title     = {Evaluation of Sentiment Analysis based on AutoML and Traditional Approaches},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {2},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {K. T.Y. Mahima and T.N.D.S.Ginige and Kasun De Zoysa},
  doi       = {10.14569/IJACSA.2021.0120277},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120277}
}