A comparative study of sentiment analysis using SVM and SentiWordNet

Mohammad Fikri, Riyanarto Sarno

Abstract


Sentiment analysis has grown rapidly which impact on the number of services using the internet popping up in Indonesia. In this research, the sentiment analysis uses the rule-based method with the help of SentiWordNet and Support Vector Machine (SVM) algorithm with Term Frequency–Inverse Document Frequency (TF-IDF) as feature extraction method. Since the number of sentences in positive, negative and neutral classes is imbalanced, the oversampling method is implemented. For imbalanced dataset, the rule-based SentiWordNet and SVM algorithm achieve accuracies of 56% and 76%, respectively. However, for the balanced dataset, the rule-based SentiWordNet and SVM algorithm achieve accuracies of 52% and 89%, respectively.


Keywords


Sentiment analysis; Sentiwordnet; Wordnet; Rule-based; Support Vector Machine

Full Text:

PDF


DOI: http://doi.org/10.11591/ijeecs.v13.i3.pp902-909
Total views : 351 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics