Table 4 Summary of decision tree (DT) techniques used in opinion mining from text

NB + DT

[45]

Find the polarity of any sentence by analysing the opinion of that particular sentence

Hindi sentences and reviews

Positive, neutral and negative

k-NN + Gaussian NB + Multinomial NB + Bernoulli NB + SVM + RBF + DT

[68]

Provide a method to overcome the problem of lower accuracy in cross-domain sentiment classification

Amazon (hotel reviews obtained from TripAdvisor reviews)

Positive or negative

CNN + NB + BFTree, OneR + LDA + SVM

[49]

Introduce and examine the proposed technique with Convolution Neural Network used for text classification

IMDB movie portal, Amazon product reviews

Positive negative and neutral

NB + LR + DT

[48]

To perform tweets classification with the help of Apache Spark framework

Twitter dataset (Kaggle and Twitter Sentiment Corpus)

Positive, negative or neutral

LR + k-NN + SVM + DT + RF + Ada Boost + Gaussian NB

[58]

To analyse the reviews posted by people at four different product websites

Amazon reviews, Yelp reviews, IMDB reviews, Indian Airlines reviews

Positive and negative

NB + DT

[30]

To apply an efficient processing approach in handling Tweets, in both Arabic and English languages

Tweets Dataset (ASTD) and Restaurant Reviews Dataset (RES)

Stanford Twitter dataset, Twitter US Airline Sentiment dataset and the Uber Ride Reviews dataset

Positive, negative and neutral

DT + Multinomial NB + SVM

[55]

To investigate three approaches for emotion classification of opinions in the Thai language

Customer reviews of cosmetics Thai

Positive and negative