Hybrid Approach with VADER and Multinomial Logistic Regression for Multiclass Sentiment Analysis in Online Customer Review

Sentiment analysis is crucial for businesses to understand customer reviews and assess sentiment polarity. A hybrid technique combining VADER and Multinomial Logistic Regression was used to analyze customer sentiment in online customer review data. VADER is a lexicon-based approach that labels reviews with sentiment using a predefined lexicon, whereas Multinomial Logistic Regression can determine the polarity of sentiment using VADER data. This study employed multiclass classification using TF-IDF vectorization to categorize sentiment as a positive, negative, or neutral class. Correctly managing neutral sentiments can assist businesses in identifying improvement opportunities. The utilization of the VADER lexicon and Multinomial Logistic Regression has been shown to significantly improve the performance of sentiment analysis in the context of multiclass classification problems. With a 75.213% accuracy rate, the VADER lexicon accurately recognizes neutral sentiment and is appropriate to adapt in categorizing sentiment related to customer reviews. Combined with Multinomial Logistic Regression, accuracy increases to 92.778%. In conclusion, the hybrid approach with VADER and Multinomial Logistic Regression can leverage the accuracy and reliability of multiclass customer sentiment analysis.

@article{Arief2023,
  title     = {Hybrid Approach with VADER and Multinomial Logistic Regression for Multiclass Sentiment Analysis in Online Customer Review},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {12},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Murahartawaty Arief and Noor Azah Samsudin},
  doi       = {10.14569/IJACSA.2023.0141232},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141232}
}