Support Vector Machine and Random Forest Machine Learning Algorithms for Sentiment Analysis on Tourism Reviews: A Performance Analysis

Manoj Kumar Sahu*, Smita Selot**
*-** Shri Sankaracharya Technical Campus, Bhilai, Chhattisgarh, India.
Periodicity:September - November'2021


People may express their opinion, attraction, and feelings through social media, which is a basic form of communication technologies. The aim of this paper is to derive different emotion behaviors, which would be used to make a strategic decision. With varying kernels and iterations, Support Vector Machine (SVM) and Random Forest (RF) are used to understand, identify, and compare tourist review results. For these data sets, the results of support vector machines and random forest are compared. The main problems in the analysis of support vector machines (SVM) is kernel selection, which is based on problem of determining a kernel function for a specific task and dataset. In this paper, SVM and RF machine learning approaches are used to analyze tourist sentiment. The effects with various kernels may be fine-tuned by proper parameter collection. The results are better analyzed in order to develop better estimation learning techniques. The proposed work have been tested using Weka machine learning tools. Experiments have shown that using 500 iterations in the 10 Folds Cross Validation testing process, RF has the highest accuracy (91.3182 %) for the dataset used.


Machine Learning, Support Vector Machine (SVM), Random Forest (RF), Sentiment Analysis, Linear, Radical Based Function.

How to Cite this Article?

Sahu, M. K., and Selot, S. (2021). Support Vector Machine and Random Forest Machine Learning Algorithms for Sentiment Analysis on Tourism Reviews: A Performance Analysis. i-manager's Journal on Computer Science, 9(3), 1-9.


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