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Table 1 Research work related to machine learning classifiers for sentiment analysis

From: An ensemble approach to stabilize the features for multi-domain sentiment analysis using supervised machine learning

Author/year Technical approach Accuracy in  % Dataset domain
Pang et al. (2002) [25] Applied N-gram model with NB, SVM, ME 77.4–82.9 Internet Movie Database (IMDb)
Dave et al. (2003) [8] Used N-gram model for feature extraction with SVM, NB classifier 87.0 Product review from Amazon and CNET
Annett and Kondrak (2008) [9] Considered WordNet as Lexical resource with SVM, NB, Decision Tree classifier 75.0 Movie reviews (IMDb)- 1000 (+) and 1000 (−) reviews
Ye et al. (2009) [12] NB, SVM classifier used for classification 85.14 Travel blogs
Mouthami et al. (2013) [11] TF-IDF and POS tagging with fuzzy classification algorithm 87.4 Movie review dataset
Zha et al. (2014) [17] SVM, NB, ME classifier adopted with evaluation matrices F1-Measure 83.0–88.43 Customer reviews (feedback)
Habernal et al. (2014) [26] N-gram and POS related features and 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) [10] Use word2vec for features with SVM classifier for classification 89.95–90.30 Chinese review dataset
Luo et al. (2016) [21] First transform the text into low dimensional emotional space (ESM), next implement SVM, NB, DT classifier 63.28–79.21 Stock message text data