Table 2 Summary of Support Vector Machine (SVM) techniques used in opinion mining from text

SVM

[25]

Design opinion classifier for classifying opinions from Bangla text data

Twitter text, English, Bangla

Positive and negative

SVM

[59]

To extract multi-class emotions from Malayalam text using the proposed approach

Malayalam text

Emotions (joy, sadness, anger, fear, surprise or normal)

SVM

[60]

To determine the expressed sentiment towards a specified aspect category in a given sentence

Yelp restaurant reviews corpus

Negative, positive and neutral

SVM

[61]

To propose and analyse new emotion identification method based on online medical knowledge-sharing community

Medical service comments

Positive and negative

SVM

[26]

To address the challenge of analysing the features of negative sentiment tweets

Twitter (TREC Microblog Track 2013)

Negative

SVM

[62]

To rank colleges based on a single feature, multiple features and no feature

Twitter (colleges)

Positive, negative or neutral sentiment

SVM

[27]

To determine the polarity of Facebook comments “positive or negative”

Facebook dataset (Tunisian political pages)

Positive and negative

SVM + RF

[31]

Determines polarity of reviews given by users and provide recommendation list

Twitter stream

Positive and negative

SVM + ANN + RF

[7]

To evaluate the thoughts of users in the IMDB movie reviews on tweets obtained from different outlets

IMDB dataset, Review Movie

Positive and negative

SVM + CRF + Multinomial NB

[53]

To present an ensemble framework of text classification which reviews products

Twitter and product review

Positive and negative

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

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

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

KNN + SVM + RF

[63]

To classify sentiments into positive, negative or neutral polarity using a new similarity measure

Stanford Twitter dataset

Positive, negative or neutral polarity

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

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

Fuzzy rule + SVM + ME

[64]

Social Media data for decision making to purchase and recommend products online

Twitter text reviews

Positive and negative