Performance Evaluation of Machine Learning Classifiers in Sentiment Mining
This provides an incremental comparison of existing methods for sentiment analysis in product reviews.
This paper evaluated machine learning classifiers for sentiment mining of Amazon product reviews, finding that support vector machine with bootstrap sampling achieved the lowest misclassification rate.
In recent years, the use of machine learning classifiers is of great value in solving a variety of problems in text classification. Sentiment mining is a kind of text classification in which, messages are classified according to sentiment orientation such as positive or negative. This paper extends the idea of evaluating the performance of various classifiers to show their effectiveness in sentiment mining of online product reviews. The product reviews are collected from Amazon reviews. To evaluate the performance of classifiers various evaluation methods like random sampling, linear sampling and bootstrap sampling are used. Our results shows that support vector machine with bootstrap sampling method outperforms others classifiers and sampling methods in terms of misclassification rate.