Sentiment Analysis: Comparative Study between GSVM and KNN

Abstract

Sentiment classification aims detecting general opinion of users in social media towards business products or daily life events. The classification tells whether sentiment is positive or negative. Techniques of sentiment classification are categorized into lexical analysis and machine learning techniques. In this paper, we propose a comparative study between SVM applied genetics (GSVM) against KNN algorithm in terms of speed and accuracy. We present also an experimental study of sentiment classification on different domains movie reviews, financial and amazon toys products. The experimental results shows that GSVM achieves a classification accuracy of 92% and KNN achieves 87% on movie reviews dataset. For classification speed, KNN shows a remarkable improvement (above 10% improvement) in comparison with GSVM.

A Machine Learning Approach to Predict Movie Revenue Based on Pre-Released Movie Metadata

“…Other architectures were also used in different researches. KNN classifier was used by Alsaffar and Omar (2015) for Malay movie reviews, a hybrid model consisting of Multi Layer Perceptron (MLP) and Naïve Bayes (NB) was proposed by Al-Batah et al (2018) for Arabic movie reviews, SVM applied genetics (GSVM) and KNN classifier was also used by Mohamed et al (2018) for the "Cornell Movie Review Dataset (polarity dataset v2.0)" (2004). Pennock (2000), also illustrated that online activities such as user comments indeed play a part in determining the financial outcome of artificial markets.…”