This study investigates a comparison of classiﬁcation models used to determine aspect based separated text sentiment and predict binary sentiments of movie reviews with genre and aspect specific driving factors. To gain a broader classiﬁcation analysis, ﬁve machine and deep learning algorithms were compared: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network Long-Short-Term Memory (RNN LSTM). The various movie aspects that are utilized to separate the sentences are determined through aggregating aspect words from lexicon-base, supervised and unsupervised learning. The driving factors are randomly assigned to various movie aspects and their impact tied to each aspect and genre leading to the sentiment classification has been fully investigated based on the accuracy of each model. The study shows that assigning higher driving factors to certain aspects and genre result in the higher accuracy of the sentiment prediction models that utilized in this research.
Onalaja, Samuel; Romero, Eric; and Yun, Bosang
"Aspect-based Sentiment Analysis of Movie Reviews,"
SMU Data Science Review: Vol. 5:
3, Article 10.
Available at: https://scholar.smu.edu/datasciencereview/vol5/iss3/10
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