Applied Computational Intelligence and Soft Computing / 2018 / Article / Tab 1

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/YearTechnical ApproachAccuracy in %Dataset domain

Pang et al. (2002)Applied N-gram model with NB, SVM, ME77.4 – 82.9 Internet Movie Database (IMDb)

Dave et al. (2003)Used N-gram model for feature extraction with SVM, NB classifier 87.0Product review from Amazon & CNET

Annett & Kondrak 
(2008)
Considered WordNet as Lexical resource with SVM, NB, Decision Tree classifier75.0Movie reviews (IMDb)- 1000 (+) and 1000 (-) reviews

Ye et al. (2009) NB, SVM classifier used for classification 85.14Travel Blogs

Mouthami et.al (2013)TF-IDF and POS tagging with fuzzy classification algorithm 87.4Movie review dataset

Zha et al. (2014)SVM, NB, ME classifier adopted with evaluation matrices F1-Measure 83.0- 88.43Customer 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.
78.50Dataset from social media

Zhang et.al. (2015)Use word2vec for features with SVM classifier for classification89.95- 90.30Chinese review dataset

Luo et.al. (2016)first transform the text into low dimensional emotional space (ESM), next implement SVM, NB, DT classifier.63.28 – 79.21Stock message text data

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