|
| Authors | Methodology | Data | Indicators | Performance result |
|
| Erdogan et al. [41] | n-gram (1, 2, 3) method, logistic regression | 2018 | Five most used cryptocurrencies in English text tweets | 94.60 |
| Ciftci et al. [42] | RNN-based algorithm | 2018 | Turkish Wikipedia articles | 83.30 |
| Coban et al. [43] | BoW vs W2VC model | 2013 | Turkish Twitter messages in the telecom sector | 59.17 |
| Ecemiş et al. [44] | Support vector machine | 2018 | Turkey-based geographical user data | 0.954 |
| Isik et al. [45] | Novel stacked ensemble method for sentiment analysis | 2018 | IMDB dataset including 1000 positive and 1000 negative; 2000 movie comments have been used | 0.791 |
| Karcioglu et al. [46] | Linear SVM and logistics regression | 2019 | Random English and Turkish texts have been collected by Twitter | 65.62 |
| Uslu et al. [47] | Logistics regression | 2019 | User reviews have been collected from Turkey’s most preferred movie site | 77.35 |
| Kanmaz et al. [48] | Decision trees, support vector machine, and Naive Bayes methods | 1996–2018 | News text-related stock exchange | 0.64–0.80 |
| Doğan et al. [49] | LSTM recurrent neural networks | 2019 | In the study, a single mixed data pool with two categories is created with data collected from multiple social networks | 0.9194–0.9266 |
| Salur et al. [50] | Random forest classification method | 2019 | Tweets collected about special tourism centers | 88.974 |
| Santur [51] | Gated recurrent unit method | 2019 | Turkish e-commerce platform user reviews | 0.955 |
| Kamis et al. [52] | Multiple CNN’s and LSTM network | 2017 | A corpus of different datasets is utilized based on three datasets used in SemEval (semantic assessment) | 0.59 |
| Ogul et al. [53] | Logistic regression classifier | 2017 | Public SemEval (semantic assessment) in three different sentiment analysis datasets containing both Turkish and English texts | 79.56 |
| Rumelli et al. [54] | k-nearest neighbor classifier | 2019 | The dataset is built by using e-commerce website (http://www.hepsiburada.com); the user review, rating, and URL of the product have been analyzed | 73.8 |
| Hayran et al. [35] | Support vector machine (SVM) classifier | 2017 | A Turkish text dataset classified (16000 positive and 16000 negative emotion) by emoji icon | 80.05 |
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