Research Article
Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Scheme for Sentiment Analysis
Table 1
Research work related to machine learning classifiers for sentiment analysis.
| Author/Year | Technical Approach | Accuracy in % | Dataset domain |
| Pang et al. (2002) | Applied N-gram model with NB, SVM, ME | 77.4 – 82.9 | Internet Movie Database (IMDb) |
| Dave et al. (2003) | Used N-gram model for feature extraction with SVM, NB classifier | 87.0 | Product review from Amazon & CNET |
| Annett & Kondrak (2008) | Considered WordNet as Lexical resource with SVM, NB, Decision Tree classifier | 75.0 | Movie reviews (IMDb)- 1000 (+) and 1000 (-) reviews |
| Ye et al. (2009) | NB, SVM classifier used for classification | 85.14 | Travel Blogs |
| Mouthami et.al (2013) | TF-IDF and POS tagging with fuzzy classification algorithm | 87.4 | Movie review dataset |
| Zha et al. (2014) | SVM, NB, ME classifier adopted with evaluation matrices F1-Measure | 83.0- 88.43 | Customer reviews (feedback) |
| Habernal et al. (2014) | n-gram and POS related features & emoticons are selected using MI, CHI, OR, RS method. Classifier ME and SVM used for classification. | 78.50 | Dataset from social media |
| Zhang et.al. (2015) | Use word2vec for features with SVM classifier for classification | 89.95- 90.30 | Chinese review dataset |
| Luo et.al. (2016) | first transform the text into low dimensional emotional space (ESM), next implement SVM, NB, DT classifier. | 63.28 – 79.21 | Stock message text data |
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