Deep Sentiment Analysis: A Case Study on Stemmed Turkish Twitter Data

Shehu, Harisu Abdullahi; Sharif, Md. Haidar; Sharif, Md Haris Uddin; Datta, Ripon; Tokat, Sezai; Uyaver, Şahin; Kusetoğulları, Hüseyin; Ramadan, Rabie A.

doi:10.1109/access.2021.3071393

Cited by 18 publications

(10 citation statements)

References 125 publications

(159 reference statements)

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“…The dataset was created with the labelling data based on emoticons; therefore, the accuracy could be little less than that of the manually labeled datasets. If we compare our study with some recent pandemic related Turkish language studies, we have nearly the same accuracy as the study [ 24 ] that used their own manually labeled benchmark dataset and worked with RNN, CNN, and HAN, which are deep learning models. However, our study differentiates with using different algorithms and datasets with different topics.…”

Section: Discussionmentioning

confidence: 96%

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Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing

Balli

Güzel

Bostancı

et al. 2022

Computational Intelligence and Neuroscience

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Artificial Intelligence has guided technological progress in recent years; it has shown significant development with increased academic studies on Machine Learning and the high demand for this field in the sector. In addition to the advancement of technology day by day, the pandemic, which has become a part of our lives since early 2020, has led to social media occupying a larger place in the lives of individuals. Therefore, social media posts have become an excellent data source for the field of sentiment analysis. The main contribution of this study is based on the Natural Language Processing method, which is one of the machine learning topics in the literature. Sentiment analysis classification is a solid example for machine learning tasks that belongs to human-machine interaction. It is essential to make the computer understand people emotional situation with classifiers. There are a limited number of Turkish language studies in the literature. Turkish language has different types of linguistic features from English. Since Turkish is an agglutinative language, it is challenging to make sentiment analysis with that language. This paper aims to perform sentiment analysis of several machine learning algorithms on Turkish language datasets that are collected from Twitter. In this research, besides using public dataset that belongs to Beyaz (2021) to get more general results, another dataset is created to understand the impact of the pandemic on people and to learn about public opinions. Therefore, a custom dataset, namely, SentimentSet (Balli 2021), was created, consisting of Turkish tweets that were filtered with words such as pandemic and corona by manually marking as positive, negative, or neutral. Besides, SentimentSet could be used in future researches as benchmark dataset. Results show classification accuracy of not only up to ∼87% with test data from datasets of both datasets and trained models, but also up to ∼84% with small “Sample Test Data” generated by the same methods as SentimentSet dataset. These research results contributed to indicating Turkish language specific sentiment analysis that is dependent on language specifications.

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Section: Discussionmentioning

confidence: 96%

“…Although there are a limited number of Turkish language sentiment analysis studies, it increased in recent years [ 24 , 25 ]. Aydogan and Kocaman [ 26 ] offered a new dataset since there are limited Turkish datasets to work on.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing

Balli

Güzel

Bostancı

et al. 2022

Computational Intelligence and Neuroscience

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“…It can be formulated using the following equation:

RES ⇒ It should ideally be 100 (the highest) for a good classifier. It can be calculated using the following equation:

AUC ⇒ It is one of the most widely used metrics for evaluation [ 177 – 179 ]. The AUC of a classifier equals the probability that the classifier ranks a randomly chosen positive sample higher than a randomly chosen negative sample.…”

Section: Preliminariesmentioning

confidence: 99%

“…It is also called the F-score or F-measure. It is used in deep learning [ 177 ]. It conveys the balance between the precision and the recall.…”

Section: Preliminariesmentioning

confidence: 99%

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Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning

Wakili

Shehu

Sharif

et al. 2022

Computational Intelligence and Neuroscience

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Breast cancer is one of the most common invading cancers in women. Analyzing breast cancer is nontrivial and may lead to disagreements among experts. Although deep learning methods achieved an excellent performance in classification tasks including breast cancer histopathological images, the existing state-of-the-art methods are computationally expensive and may overfit due to extracting features from in-distribution images. In this paper, our contribution is mainly twofold. First, we perform a short survey on deep-learning-based models for classifying histopathological images to investigate the most popular and optimized training-testing ratios. Our findings reveal that the most popular training-testing ratio for histopathological image classification is 70%: 30%, whereas the best performance (e.g., accuracy) is achieved by using the training-testing ratio of 80%: 20% on an identical dataset. Second, we propose a method named DenTnet to classify breast cancer histopathological images chiefly. DenTnet utilizes the principle of transfer learning to solve the problem of extracting features from the same distribution using DenseNet as a backbone model. The proposed DenTnet method is shown to be superior in comparison to a number of leading deep learning methods in terms of detection accuracy (up to 99.28% on BreaKHis dataset deeming training-testing ratio of 80%: 20%) with good generalization ability and computational speed. The limitation of existing methods including the requirement of high computation and utilization of the same feature distribution is mitigated by dint of the DenTnet.

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Substituting clinical features using synthetic medical phrases: Medical text data augmentation techniques

Abdollahi

Gao

Ghosh³

et al. 2021

Artificial Intelligence in Medicine

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Deep Sentiment Analysis: A Case Study on Stemmed Turkish Twitter Data

Cited by 18 publications

References 125 publications

Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing

Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing

Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning

Substituting clinical features using synthetic medical phrases: Medical text data augmentation techniques

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