| dc.description.abstract | Sentiment analysis is the interpretation and classification of emotion and opinions from the text. The scale of emotions and opinions can vary from positive to negative and maybe neutral. Customer sentiment analysis helps businesses point out the public’s thoughts and feelings about their products, brands or services in online conversations and feedback. Natural language processing and text classification are crucial for sentiment analysis. That means we can predict or classify customers’ opinions given their comments.
In this research, we do sentiment analysis in the two different movie review datasets using various machine learning techniques including Decision tree, Naïve Bayes, Support Vector Machine, Blending, Voting, and Recurrent Neural Networks (RNN). We propose a few frameworks of sentiment classification using these techniques on the given datasets. Several experiments are conducted to evaluate them compared with an outstanding natural language processing tool (Stanford CoreNLP) at present.
The experimental results have shown our proposals can achieve higher performance. Especially, the Voting and RNN-based classification models can result in better predictions.
Keywords: Sentiment Analysis, Opinion Mining, Classification, Stanford CoreNLP . | en_US |
