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Table 8 Comparisons of performance of proposed approach with different literature using different domain review Dataset

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

  Dataset Feature selection method Classifier Performance
Pang et al. [29] Internet Movie Database (IMDb) N-gram features SVM
NB
ME
82.9 (Accuracy)
81.5
81.0
Agarwal et al. [1] Movie (IMDb)
Product (book, DVD, electronics)
N-gram, IG, RSAR,
Hybrid(IG + RSAR)
SVM
NB
87.7 (F measure)
80.9
Al-Moslmi et al. [44] Movie reviews in the Malay language IG, CHI, Gini Index SVM
NB
KNN
85.33 (F-measure)
80.88
74.68
Kolog et al. [18] Sentiment from social network regarding student’s life N gram features SMO
MNB
J48
80.0
83.0
69.0
Tripathy et al. [22, 43] Movie (IMDb) N-gram features SVM
ME
NB
SGD
88.94
88.48
86.23
85.11
Our approach Movie (IMDb)
Electronics product
Kitchenware
N-gram, Combination of Unigram and bigram with IG, CHI, Gini Index SMO
MNB
RF
LR
90.18 (F-measure)
88.18
87.73
87.32