Table 1 Summary of Naïve Bayes/Bayesian techniques used in opinion mining from text

NB

[6]

To present a continuous Naïve Bayes learning framework for e-commerce product review sentiment classification

E-commerce review and Cornell Movie review dataset

Positive, negative and neutral

[24]

To develop a workflow for applying sentiment analysis in detecting public emotions in natural disaster crises

Twitter (Kashmir Floods)

Negative, positive and neutral

[23]

To explore consumer attitudes and experiences of "train operating companies."

Twitter (tweets on train operating companies)

Positive or negative

[36]

To access and classify Tweets for counter violent extremism and the spread of extremist content on Twitter

Twitter Data

Positive, negative and neutral

[37]

To investigate tourist emotions on their travel experiences targeting Gatlinburg, Tennessee

Online reviews of Tripadvisor

Emotions (anger, disgust, fear, joy, sadness and surprise)

[21]

To analyse every food review of the user and classify if it is positive, negative or neutral

McDonald’s dataset is customer reviews

Positive, negative and neutral

[38]

To monitor public opinion on trending topics on the social media platform

Twitter

Positive, negative or neutral

[39]

To perform aspect-based sentiment analysis by filtering statements from the review pertinent and extracting sentiments from the reviews, and associating them with corresponding aspect categories

Amazon movie review dataset

Positivity or negativity

NB + SVM

[40]

Analyse opinions on smartphone reviews

Smartphone reviews

Positive and negative

[41]

Survey different types of sentiment analysis methods based on cryptocurrencies topic

Twitter

Positive, neutral and negative

[42]

Identify the levels of positive and negative emotion in messages

Twitter comment,

unrelated, neutral, negative and positive messages

[29]

To develop a polarity detection system on textual movie reviews in Bangla

Text movie review in Bangla

Positive or negative

[28]

To implement a combination of user behaviour, semantic and lexical features together for finding polarity emotions of Tweets

Twitter

Positive and negative

[43]

To analyse and consider traffic jam events where traffic will be able to move or will not be able to move

Twitter (traffic jams)

Positive, negative or neutral

NB + SVM + DBN

[44]

To classify a Malay sentiment by proposing a classification model to improve classification performances

Online blogs and forums of Malaysian website

Positive and negative

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

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

NB + ME

[46]

To evaluate the accuracy of combining different parameters of machine-learning algorithms for consumer products

Twitter

Positive or negative

NB + ME + SGD + SVM

[47]

To classify human sentiment-based movie reviews using various supervised machine learning algorithms

To examine the accuracy of different methods

Internet Movie Database (IMDB)

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

CNN + NB + J48 (DT) + 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

SVM + NB + RF

[20]

To provide sentiment mining in extracted sentiment from Twitter Social App for analysis of the current trending topic in India and its impact on different sectors of the Indian economy

Tweets

Positive, negative and neutral

SVM + NB + RF

[50]

Mining consumer reviews with a machine learning approach by converting reviews into vector representations for classification

Amazon review dataset

Positive or negative

Multinomial NB + SVM

[51]

Develop an efficient review classification

Reviews TripAdvisor dataset

Positive and negative

SVM + Multinomial NB + LR + RF

[52]

To develop a clinical decision support system for the personalised therapy process

Drug review dataset

Positive, negative or neutral

SVM + CRF + Multinomial NB

[53]

To present an ensemble framework of text classification which reviews products

Twitter and product review

Positive and negative

Multinomial NB + SVM + LR

[54]

To compare the performance of different machine learning algorithms in performing sentiment analysis of Twitter data

Twitter

Positive or negative

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

SVM + Multinomial NB + DNN

[56]

To compare multiple state-of-the-art models capable of classifying game reviews as positive, negative or neutral

Games reviews

Positive, neutral and negative

Bernoulli NB + SVM + RF + NNs + LR

[57]

To present a comparison among several sentiment analysis classifiers in the learning environment

Twitter (educational opinions in an Intelligent Learning Environment)

Emotions positive or negative, engagement, excited, boredom and frustration

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