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

Luo (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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