My research intererests are NLP, Machine Learning, Text Mining, Social Network, Data Analysis. I use WEKA, Git, Keras Tensorflow, Gensim, NLTK, Polyglot, Scikit-learn, Pymongo, Py2neo, Matplotlib, Plotly, PyTorch with Python. Address: Department of Computer Engineering & Ege University
2019 4th International Conference on Computer Science and Engineering (UBMK), 2019
Today, with the increase of Internet-based documents, we are presented with many data that need t... more Today, with the increase of Internet-based documents, we are presented with many data that need to be processed and evaluated. Media, news and advertising are some of the areas where these data are evaluated. For the news, the classification of people in the media sector is an important problem in terms of time. In this paper, it is aimed to determine which types of news titles belong to. The dataset consists of 4200 Turkish new titles belonging to 7 class labels. In order to determine the types, classical Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA) and Non-Negative Matrix Factorization (NMF) algorithms were used in topic modeling. In addition, the LDA-based n-LDA method was also used. The accuracy of all methods used was measured and compared. NMF was the most successful method for three classes, while for five and seven classes LSA was the most successful method.
2019 Innovations in Intelligent Systems and Applications Conference (ASYU), 2019
The development of technology has enabled the use of new ways and methods to determine the emotio... more The development of technology has enabled the use of new ways and methods to determine the emotion of sharing on social media. For areas such as media and advertising, social media plays an important role today. In this study, Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) methods in subject modeling were used to determine the emotions of tweets thrown through Twitter. In addition, the model was also supported by an LDA-based method to increase the success of the system. The dataset consists of 5 emotions; angry, fear, happy, sadness and surprised. The success of all topic modeling methods used in the study was measured and most successful method was NMF. Then, success of the machine learning algorithms were measured by creating file according to Weka with word weights and class label of the topics. The most successful method was n-stage LDA while the most successful algorithm was Random Forest.
2021 6th International Conference on Computer Science and Engineering (UBMK)
Nowadays, shopping is done more comfortably and without time constraints with the throwing of e-c... more Nowadays, shopping is done more comfortably and without time constraints with the throwing of e-commerce platforms. These platforms allow consumers to examine reviews before purchasing products. Thus, consumers can decide whether to buy a product with positive or negative comments about the products. In this paper, Turkish sentiment analysis was carried out on the product comments at the Hepsiburada platform. For sentiment analysis, firstly, the success of Random Forest, Naive Bayes and Logistic Regression machine learning methods was measured. Then, the effect of BERT, ELECTRA and ALBERT language models on sentiment analysis was analyzed and the success of language models was compared with machine learning methods. While Naive Bayes achieved the highest accuracy with 89.95% among machine learning methods, ELECTRA was the most successful with 92.54% among language models. As a result of the study, it has been shown that the ELECTRA and ALBERT language models are more successful than machine learning methods.
2021 6th International Conference on Computer Science and Engineering (UBMK)
Recently, the analysis of textual data has gained importance due to the increase in comments made... more Recently, the analysis of textual data has gained importance due to the increase in comments made on web platforms and the need for ready-made answering systems. Therefore, there are many studies in the fields of natural language processing such as text summarization and question answering. In this paper, the accuracy of the BERT language model is analyzed for the question answering domain, which allows to automatically answer a question asked. Using SQuAD, one of the reading comprehension datasets, the answers to the questions that the BERT model cannot answer are researched with the proposed Named Entity Recognition method in natural language processing. The accuracy of BERT models used with the proposed Named Entity Recognition method increases between 1.7% and 2.7%. As a result of the analysis, it is shown that the BERT model doesn't use Named Entity Recognition technique sufficiently.
2021 6th International Conference on Computer Science and Engineering (UBMK)
Users can freely express their opinions about many events on social media platforms. It may be ne... more Users can freely express their opinions about many events on social media platforms. It may be necessary to analyze the data in order to get the opinion of the society about these events. Therefore, sentiment analysis studies are gaining importance today. Many different methods and models are used for sentiment analysis. While language models such as the BERT model are widely used in the English language, there are very few studies for the Turkish language in sentiment analysis. In this study, sentiment analysis was performed on tweets using BERT models and machine learning methods. In addition, the trained BERT models and machine learning methods were compared. Among the Random Forest, Naive Bayes and Logistic Regression machine learning methods, Logistic Regression was the most successful method with 98.4%. BERT models achieved 98.75% accuracy and surpassed the success of machine learning methods. The positive effect of the BERT model on sentiment analysis was shown with this study.
With the development of technology, the use of social media has become quite common. Analyzing co... more With the development of technology, the use of social media has become quite common. Analyzing comments on social media in areas such as media and advertising plays an important role today. For this reason, new and traditional natural language processing methods are used to detect the emotion of these shares. In this paper, the Latent Dirichlet Allocation, namely LDA, and Non-Negative Matrix Factorization methods in topic modeling were used to determine which emotion the Turkish tweets posted via Twitter. In addition, the accuracy of a proposed n-level method based on LDA was analyzed. Dataset consists of 5 emotions, namely angry, fear, happy, sad and confused. NMF was the most successful method among all topic modeling methods in this study. Then, the F1-measure of Random Forest, Naive Bayes and Support Vector Machine methods was analyzed by obtaining a file suitable for Weka by using the word weights and class labels of the topics. Among the Weka results, the most successful method was n-stage LDA, and the most successful algorithm was Random Forest.
Nowadays, data analysis has become a problem as the amount of data is constantly increasing. In o... more Nowadays, data analysis has become a problem as the amount of data is constantly increasing. In order to overcome this problem in textual data, many models and methods are used in natural language processing. The topic modeling field is one of these methods. Topic modeling allows determining the semantic structure of a text document. Latent Dirichlet Allocation (LDA) is the most common method among topic modeling methods. In this article, the proposed n-stage LDA method, which can enable the LDA method to be used more effectively, is explained in detail. The positive effect of the method has been demonstrated by the applied English and Turkish studies. Since the method focuses on reducing the word count in the dictionary, it can be used languageindependently. You can access the open-source code of the method and the example: https: //github.com/anil1055/n-stage_LDA
6th International Conference on Computer Science and Engineering (UBMK), 2021
Nowadays, shopping is done more comfortably and without time constraints with the throwing of e-c... more Nowadays, shopping is done more comfortably and without time constraints with the throwing of e-commerce platforms. These platforms allow consumers to examine reviews before purchasing products. Thus, consumers can decide whether to buy a product with positive or negative comments about the products. In this paper, Turkish sentiment analysis was carried out on the product comments at the Hepsiburada platform. For sentiment analysis, firstly, the success of Random Forest, Naive Bayes and Logistic Regression machine learning methods was measured. Then, the effect of BERT, ELECTRA and ALBERT language models on sentiment analysis was analyzed and the success of language models was compared with machine learning methods. While Naive Bayes achieved the highest accuracy with 89.95% among machine learning methods, ELECTRA was the most successful with 92.54% among language models. As a result of the study, it has been shown that the ELECTRA and ALBERT language models are more successful than machine learning methods.
6th International Conference on Computer Science and Engineering (UBMK), 2021
Recently, the analysis of textual data has gained importance due to the increase in comments made... more Recently, the analysis of textual data has gained importance due to the increase in comments made on web platforms and the need for ready-made answering systems. Therefore, there are many studies in the fields of natural language processing such as text summarization and question answering. In this paper, the accuracy of the BERT language model is analyzed for the question answering domain, which allows to automatically answer a question asked. Using SQuAD, one of the reading comprehension datasets, the answers to the questions that the BERT model cannot answer are researched with the proposed Named Entity Recognition method in natural language processing. The accuracy of BERT models used with the proposed Named Entity Recognition method increases between 1.7% and 2.7%. As a result of the analysis, it is shown that the BERT model doesn't use Named Entity Recognition technique sufficiently.
6th International Conference on Computer Science and Engineering (UBMK), 2021
Users can freely express their opinions about many events on social media platforms. It may be ne... more Users can freely express their opinions about many events on social media platforms. It may be necessary to analyze the data in order to get the opinion of the society about these events. Therefore, sentiment analysis studies are gaining importance today. Many different methods and models are used for sentiment analysis. While language models such as the BERT model are widely used in the English language, there are very few studies for the Turkish language in sentiment analysis. In this study, sentiment analysis was performed on tweets using BERT models and machine learning methods. In addition, the trained BERT models and machine learning methods were compared. Among the Random Forest, Naive Bayes and Logistic Regression machine learning methods, Logistic Regression was the most successful method with 98.4%. BERT models achieved 98.75% accuracy and surpassed the success of machine learning methods. The positive effect of the BERT model on sentiment analysis was shown with this study.
4th International Conference on Computer Science and Engineering (UBMK), 2019
Today, with the increase of Internet-based documents, we are presented with many ... more Today, with the increase of Internet-based documents, we are presented with many data that need to be processed and evaluated. Media, news and advertising are some of the areas where these data are evaluated. For the news, the classification of people in the media sector is an important problem in terms of time. In this paper, it is aimed to determine which types of news titles belong to. The dataset consists of 4200 Turkish new titles belonging to 7 class labels. In order to determine the types, classical Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA) and Non-Negative Matrix Factorization (NMF) algorithms were used in topic modeling. In addition, the LDA-based n-LDA method was also used. The accuracy of all methods used was measured and compared. NMF was the most successful method for three classes, while for five and seven classes LSA was the most successful method.
2019 Innovations in Intelligent Systems and Applications Conference (ASYU), 2019
The development of technology has enabled the use of new ways and methods to determi... more The development of technology has enabled the use of new ways and methods to determine the emotion of sharing on social media. For areas such as media and advertising, social media plays an important role today. In this study, Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) methods in subject modeling were used to determine the emotions of tweets thrown through Twitter. In addition, the model was also supported by an LDA-based method to increase the success of the system. The dataset consists of 5 emotions; angry, fear, happy, sadness and surprised. The success of all topic modeling methods used in the study was measured and most successful method was NMF. Then, success of the machine learning algorithms were measured by creating file according to Weka with word weights and class label of the topics. The most successful method was n-stage LDA while the most successful algorithm was Random Forest.
Journal of the Faculty of Engineering and Architecture of Gazi University, 2020
Understanding the emotions of sharing in social media plays a key role in learning people's thoug... more Understanding the emotions of sharing in social media plays a key role in learning people's thoughts. Knowing the emotion of human beings with developing technology provides benefits in various fields. For example, media, marketing and advertising areas allow people to reflect on their use and idea-specific content. In our study, Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA) and Probabilistic-Latent Semantic Analysis (P-LSA) were used to determine the emotions of individuals from Turkish tweets. In addition, the success of the developed n-stage state of the LDA algorithm in the emotion analysis was compared with the existing methods. The dataset consists of 4000 tweets of 5 different emotions, including angry, fear, happiness, sadness and surprise. All topic modeling methods were modeled for 3 and 5 class datasets and their successes and running times were measured. It has been observed that the developed n-stage LDA method achieves success in terms of running time and performance according to LDA and P-LSA. The most successful and fastest modeled method was LSA.
Academic Platform Journal of Engineering and Science, Sep 30, 2019
Understanding the reason behind the emotions placed in the social media plays a key role to learn... more Understanding the reason behind the emotions placed in the social media plays a key role to learn mood characterization of any written texts that are not seen before. Knowing how to classify the mood characterization leads this technology to be useful in a variety of fields. The Latent Dirichlet Allocation (LDA), a topic modeling algorithm, was used to determine which emotions the tweets on Twitter had in the study. The dataset consists of 4000 tweets that are categorized into 5 different emotions that are anger, fear, happiness, sadness, and surprise. Zemberek, Snowball, and first 5 letters root extraction methods are used to create models. The generated models were tested by using the proposed n-stage LDA method. With the proposed method, we aimed to increase model’s success rate by decreasing the number of words in the dictionary. Using the multi-stage LDA (2-stages:70.5%, 3-stages:76.375%) method, the success rate was increased compared to normal LDA (60.375%) for 5 class.
Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), 2018
Abstract-The classification of the emotions contained in the social media is of great importance ... more Abstract-The classification of the emotions contained in the social media is of great importance in terms of its use in related fields such as media as well as developing technology. The Latent Dirichlet Allocation (LDA), a topic modeling algorithm, was used to determine which emotions the tweets on Twitter had in the study. Dataset consists of angry, fear, happy, sadness and surprise, 5 emotions and 4000 tweets. Zemberek, Snowball and the first 5 letter root extraction methods are used to create the model. The generated models were tested with the n-stage LDA method we developed and compared with the LDA. For the 5 classes of normal LDA method, the highest 60.4% success was achieved; 70.5% for 2-stage LDA and 76.4% for 3-stage LDA.
Innovations in Intelligent Systems and Applications Conference (ASYU), 2018
Abstract-With the rapid development of the Internet, thousands of different news reports from dif... more Abstract-With the rapid development of the Internet, thousands of different news reports from different channels are presented to us. So much news, particularly in the media sector, is an important question to be categorized and archived without human effort. In this study, it is aimed to be able to determine which news item belongs to large news headlines collected from news sites. For this, a two stage method is proposed, which is based on the classical Latent Dirichlet Allocation (LDA) algorithm used in the model. With the developed two stage LDA method, comparison of the conventional LDA was made. Then, by creating a file with an arff extension from the word weights of the topics, the success of the machine learning methods in Weka was measured.
2019 4th International Conference on Computer Science and Engineering (UBMK), 2019
Today, with the increase of Internet-based documents, we are presented with many data that need t... more Today, with the increase of Internet-based documents, we are presented with many data that need to be processed and evaluated. Media, news and advertising are some of the areas where these data are evaluated. For the news, the classification of people in the media sector is an important problem in terms of time. In this paper, it is aimed to determine which types of news titles belong to. The dataset consists of 4200 Turkish new titles belonging to 7 class labels. In order to determine the types, classical Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA) and Non-Negative Matrix Factorization (NMF) algorithms were used in topic modeling. In addition, the LDA-based n-LDA method was also used. The accuracy of all methods used was measured and compared. NMF was the most successful method for three classes, while for five and seven classes LSA was the most successful method.
2019 Innovations in Intelligent Systems and Applications Conference (ASYU), 2019
The development of technology has enabled the use of new ways and methods to determine the emotio... more The development of technology has enabled the use of new ways and methods to determine the emotion of sharing on social media. For areas such as media and advertising, social media plays an important role today. In this study, Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) methods in subject modeling were used to determine the emotions of tweets thrown through Twitter. In addition, the model was also supported by an LDA-based method to increase the success of the system. The dataset consists of 5 emotions; angry, fear, happy, sadness and surprised. The success of all topic modeling methods used in the study was measured and most successful method was NMF. Then, success of the machine learning algorithms were measured by creating file according to Weka with word weights and class label of the topics. The most successful method was n-stage LDA while the most successful algorithm was Random Forest.
2021 6th International Conference on Computer Science and Engineering (UBMK)
Nowadays, shopping is done more comfortably and without time constraints with the throwing of e-c... more Nowadays, shopping is done more comfortably and without time constraints with the throwing of e-commerce platforms. These platforms allow consumers to examine reviews before purchasing products. Thus, consumers can decide whether to buy a product with positive or negative comments about the products. In this paper, Turkish sentiment analysis was carried out on the product comments at the Hepsiburada platform. For sentiment analysis, firstly, the success of Random Forest, Naive Bayes and Logistic Regression machine learning methods was measured. Then, the effect of BERT, ELECTRA and ALBERT language models on sentiment analysis was analyzed and the success of language models was compared with machine learning methods. While Naive Bayes achieved the highest accuracy with 89.95% among machine learning methods, ELECTRA was the most successful with 92.54% among language models. As a result of the study, it has been shown that the ELECTRA and ALBERT language models are more successful than machine learning methods.
2021 6th International Conference on Computer Science and Engineering (UBMK)
Recently, the analysis of textual data has gained importance due to the increase in comments made... more Recently, the analysis of textual data has gained importance due to the increase in comments made on web platforms and the need for ready-made answering systems. Therefore, there are many studies in the fields of natural language processing such as text summarization and question answering. In this paper, the accuracy of the BERT language model is analyzed for the question answering domain, which allows to automatically answer a question asked. Using SQuAD, one of the reading comprehension datasets, the answers to the questions that the BERT model cannot answer are researched with the proposed Named Entity Recognition method in natural language processing. The accuracy of BERT models used with the proposed Named Entity Recognition method increases between 1.7% and 2.7%. As a result of the analysis, it is shown that the BERT model doesn't use Named Entity Recognition technique sufficiently.
2021 6th International Conference on Computer Science and Engineering (UBMK)
Users can freely express their opinions about many events on social media platforms. It may be ne... more Users can freely express their opinions about many events on social media platforms. It may be necessary to analyze the data in order to get the opinion of the society about these events. Therefore, sentiment analysis studies are gaining importance today. Many different methods and models are used for sentiment analysis. While language models such as the BERT model are widely used in the English language, there are very few studies for the Turkish language in sentiment analysis. In this study, sentiment analysis was performed on tweets using BERT models and machine learning methods. In addition, the trained BERT models and machine learning methods were compared. Among the Random Forest, Naive Bayes and Logistic Regression machine learning methods, Logistic Regression was the most successful method with 98.4%. BERT models achieved 98.75% accuracy and surpassed the success of machine learning methods. The positive effect of the BERT model on sentiment analysis was shown with this study.
With the development of technology, the use of social media has become quite common. Analyzing co... more With the development of technology, the use of social media has become quite common. Analyzing comments on social media in areas such as media and advertising plays an important role today. For this reason, new and traditional natural language processing methods are used to detect the emotion of these shares. In this paper, the Latent Dirichlet Allocation, namely LDA, and Non-Negative Matrix Factorization methods in topic modeling were used to determine which emotion the Turkish tweets posted via Twitter. In addition, the accuracy of a proposed n-level method based on LDA was analyzed. Dataset consists of 5 emotions, namely angry, fear, happy, sad and confused. NMF was the most successful method among all topic modeling methods in this study. Then, the F1-measure of Random Forest, Naive Bayes and Support Vector Machine methods was analyzed by obtaining a file suitable for Weka by using the word weights and class labels of the topics. Among the Weka results, the most successful method was n-stage LDA, and the most successful algorithm was Random Forest.
Nowadays, data analysis has become a problem as the amount of data is constantly increasing. In o... more Nowadays, data analysis has become a problem as the amount of data is constantly increasing. In order to overcome this problem in textual data, many models and methods are used in natural language processing. The topic modeling field is one of these methods. Topic modeling allows determining the semantic structure of a text document. Latent Dirichlet Allocation (LDA) is the most common method among topic modeling methods. In this article, the proposed n-stage LDA method, which can enable the LDA method to be used more effectively, is explained in detail. The positive effect of the method has been demonstrated by the applied English and Turkish studies. Since the method focuses on reducing the word count in the dictionary, it can be used languageindependently. You can access the open-source code of the method and the example: https: //github.com/anil1055/n-stage_LDA
6th International Conference on Computer Science and Engineering (UBMK), 2021
Nowadays, shopping is done more comfortably and without time constraints with the throwing of e-c... more Nowadays, shopping is done more comfortably and without time constraints with the throwing of e-commerce platforms. These platforms allow consumers to examine reviews before purchasing products. Thus, consumers can decide whether to buy a product with positive or negative comments about the products. In this paper, Turkish sentiment analysis was carried out on the product comments at the Hepsiburada platform. For sentiment analysis, firstly, the success of Random Forest, Naive Bayes and Logistic Regression machine learning methods was measured. Then, the effect of BERT, ELECTRA and ALBERT language models on sentiment analysis was analyzed and the success of language models was compared with machine learning methods. While Naive Bayes achieved the highest accuracy with 89.95% among machine learning methods, ELECTRA was the most successful with 92.54% among language models. As a result of the study, it has been shown that the ELECTRA and ALBERT language models are more successful than machine learning methods.
6th International Conference on Computer Science and Engineering (UBMK), 2021
Recently, the analysis of textual data has gained importance due to the increase in comments made... more Recently, the analysis of textual data has gained importance due to the increase in comments made on web platforms and the need for ready-made answering systems. Therefore, there are many studies in the fields of natural language processing such as text summarization and question answering. In this paper, the accuracy of the BERT language model is analyzed for the question answering domain, which allows to automatically answer a question asked. Using SQuAD, one of the reading comprehension datasets, the answers to the questions that the BERT model cannot answer are researched with the proposed Named Entity Recognition method in natural language processing. The accuracy of BERT models used with the proposed Named Entity Recognition method increases between 1.7% and 2.7%. As a result of the analysis, it is shown that the BERT model doesn't use Named Entity Recognition technique sufficiently.
6th International Conference on Computer Science and Engineering (UBMK), 2021
Users can freely express their opinions about many events on social media platforms. It may be ne... more Users can freely express their opinions about many events on social media platforms. It may be necessary to analyze the data in order to get the opinion of the society about these events. Therefore, sentiment analysis studies are gaining importance today. Many different methods and models are used for sentiment analysis. While language models such as the BERT model are widely used in the English language, there are very few studies for the Turkish language in sentiment analysis. In this study, sentiment analysis was performed on tweets using BERT models and machine learning methods. In addition, the trained BERT models and machine learning methods were compared. Among the Random Forest, Naive Bayes and Logistic Regression machine learning methods, Logistic Regression was the most successful method with 98.4%. BERT models achieved 98.75% accuracy and surpassed the success of machine learning methods. The positive effect of the BERT model on sentiment analysis was shown with this study.
4th International Conference on Computer Science and Engineering (UBMK), 2019
Today, with the increase of Internet-based documents, we are presented with many ... more Today, with the increase of Internet-based documents, we are presented with many data that need to be processed and evaluated. Media, news and advertising are some of the areas where these data are evaluated. For the news, the classification of people in the media sector is an important problem in terms of time. In this paper, it is aimed to determine which types of news titles belong to. The dataset consists of 4200 Turkish new titles belonging to 7 class labels. In order to determine the types, classical Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA) and Non-Negative Matrix Factorization (NMF) algorithms were used in topic modeling. In addition, the LDA-based n-LDA method was also used. The accuracy of all methods used was measured and compared. NMF was the most successful method for three classes, while for five and seven classes LSA was the most successful method.
2019 Innovations in Intelligent Systems and Applications Conference (ASYU), 2019
The development of technology has enabled the use of new ways and methods to determi... more The development of technology has enabled the use of new ways and methods to determine the emotion of sharing on social media. For areas such as media and advertising, social media plays an important role today. In this study, Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) methods in subject modeling were used to determine the emotions of tweets thrown through Twitter. In addition, the model was also supported by an LDA-based method to increase the success of the system. The dataset consists of 5 emotions; angry, fear, happy, sadness and surprised. The success of all topic modeling methods used in the study was measured and most successful method was NMF. Then, success of the machine learning algorithms were measured by creating file according to Weka with word weights and class label of the topics. The most successful method was n-stage LDA while the most successful algorithm was Random Forest.
Journal of the Faculty of Engineering and Architecture of Gazi University, 2020
Understanding the emotions of sharing in social media plays a key role in learning people's thoug... more Understanding the emotions of sharing in social media plays a key role in learning people's thoughts. Knowing the emotion of human beings with developing technology provides benefits in various fields. For example, media, marketing and advertising areas allow people to reflect on their use and idea-specific content. In our study, Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA) and Probabilistic-Latent Semantic Analysis (P-LSA) were used to determine the emotions of individuals from Turkish tweets. In addition, the success of the developed n-stage state of the LDA algorithm in the emotion analysis was compared with the existing methods. The dataset consists of 4000 tweets of 5 different emotions, including angry, fear, happiness, sadness and surprise. All topic modeling methods were modeled for 3 and 5 class datasets and their successes and running times were measured. It has been observed that the developed n-stage LDA method achieves success in terms of running time and performance according to LDA and P-LSA. The most successful and fastest modeled method was LSA.
Academic Platform Journal of Engineering and Science, Sep 30, 2019
Understanding the reason behind the emotions placed in the social media plays a key role to learn... more Understanding the reason behind the emotions placed in the social media plays a key role to learn mood characterization of any written texts that are not seen before. Knowing how to classify the mood characterization leads this technology to be useful in a variety of fields. The Latent Dirichlet Allocation (LDA), a topic modeling algorithm, was used to determine which emotions the tweets on Twitter had in the study. The dataset consists of 4000 tweets that are categorized into 5 different emotions that are anger, fear, happiness, sadness, and surprise. Zemberek, Snowball, and first 5 letters root extraction methods are used to create models. The generated models were tested by using the proposed n-stage LDA method. With the proposed method, we aimed to increase model’s success rate by decreasing the number of words in the dictionary. Using the multi-stage LDA (2-stages:70.5%, 3-stages:76.375%) method, the success rate was increased compared to normal LDA (60.375%) for 5 class.
Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), 2018
Abstract-The classification of the emotions contained in the social media is of great importance ... more Abstract-The classification of the emotions contained in the social media is of great importance in terms of its use in related fields such as media as well as developing technology. The Latent Dirichlet Allocation (LDA), a topic modeling algorithm, was used to determine which emotions the tweets on Twitter had in the study. Dataset consists of angry, fear, happy, sadness and surprise, 5 emotions and 4000 tweets. Zemberek, Snowball and the first 5 letter root extraction methods are used to create the model. The generated models were tested with the n-stage LDA method we developed and compared with the LDA. For the 5 classes of normal LDA method, the highest 60.4% success was achieved; 70.5% for 2-stage LDA and 76.4% for 3-stage LDA.
Innovations in Intelligent Systems and Applications Conference (ASYU), 2018
Abstract-With the rapid development of the Internet, thousands of different news reports from dif... more Abstract-With the rapid development of the Internet, thousands of different news reports from different channels are presented to us. So much news, particularly in the media sector, is an important question to be categorized and archived without human effort. In this study, it is aimed to be able to determine which news item belongs to large news headlines collected from news sites. For this, a two stage method is proposed, which is based on the classical Latent Dirichlet Allocation (LDA) algorithm used in the model. With the developed two stage LDA method, comparison of the conventional LDA was made. Then, by creating a file with an arff extension from the word weights of the topics, the success of the machine learning methods in Weka was measured.
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