Table 1.
Comprehensive comparative analysis of related works in sentiment analysis for the Arabic language.
No | Algorithms | Data Source | Features | Best Result |
---|---|---|---|---|
[11] | NB | Mubasher Software Products | TF-IDF | NB 88.81% |
SVM | BOW | |||
[12] | LR | Different Sources | TF-IDF | DT 92% |
KNN | BOW | |||
DT | ||||
[13] | SVM | Corpus of Saudi Tweets | TF-IDF | SVM85.25% |
KNN | ||||
NB | ||||
DT | BOW | |||
Deep Learning | ||||
[14] | SVM | Amazon Prime | TF-IDF | MLP 92.00% |
NB | BOW | |||
DT | ||||
LR | W2Vec | |||
RF | ||||
MLP | ||||
[15] | SVM | Customer reviews from cafes and restaurant | TF-IDF | SVM89% |
LR | ||||
KNN | ||||
NB | ||||
RF | ||||
DT | ||||
NB | ||||
This Study | KNN | Reviews Customer Perceptions (coffee) | TF-IDF | SVM 94.95% |
SVM | ||||
DT | MRMR | |||
RF |