Performance Evaluation of Machine Learning Classifiers in Sentiment Mining

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.Comment: 4 pages 2 tables, International Journal of Computer Trends and Technology, volume 4, Issue 6, june 201

NLPR at Multilingual Opinion Analysis Task in NTCIR7

This paper presents our work in the simplified Chinese opinion analysis task in NTCIR7. For identifying the subjective sentences, the domain adaptation technique was applied in our method, so that the data in NTCIR6 can be used for training subjective classifier. The evaluation results proves that the method proposed in this paper is effective. In extracting the opinion holder, we used the CRF model, which was combined with manual designed heuristics rules. For CRF model we not only extracted part-of-speech features, semantic class features, contextual features, but also some dependency features through parsing analysis. The evaluation results prove that the proposed method is effective for extracting opinion holders