Text Mining Analysis in Turkish Language Using Big Data Tools

Cakir, Mehmet Ulas; Guldamlasioglu, Seren

doi:10.1109/compsac.2016.203

Cited by 21 publications

(7 citation statements)

References 12 publications

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“…Furthermore, the word vectors are proportioned by their tf-idf values and then averaged in order to have a single review vector for each review. Then, an extreme gradient boosting (Xgboost) [14] classifier is trained on [15] review vectors of labeled customer reviews for the task of binary sentiment analysis, i.e. classification of positive and negative sentiments.…”

Section: Methodsmentioning

confidence: 99%

A Hierarchical Approach for Sentiment Analysis and Categorization of Turkish Written Customer Relationship Management Data

Seyfioğlu¹,

Demirezen²

2017

Annals of Computer Science and Information Systems

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Today, large scale companies are receiving tens of thousands of feedback from their customers every day, which makes it impossible for them to evaluate the feedbacks manually. As sentiments expressed by the customers are vitally important for companies, an accurate and swift analysis is needed. In this paper, a hierarchical approach is proposed for sentiment analysis and further categorization of Turkish written customer feedback to a private airline company. First, the word embeddings of customer feedbacks are computed by using Word2Vec then averaged in proportion with the inverse of their frequency in the document. For binary sentiment analysis, i.e determination of 'positive' and 'negative' sentiments, an extreme gradient boosting (xgboost) classifier is trained on averaged review vectors and an overall accuracy of 92.5% is obtained which is 16.8% higher than that of the baseline model. For further categorization of negative sentiments in one of twelve pre determined classes, an xgboost classifier is trained upon document embeddings of negatively classified comments, which were calculated using Doc2Vec. An overall accuracy of 71.16% is obtained for the task of categorization of 12 different classes using the Doc2Vec approach, thereby yielding a classification accuracy 19.1% higher than that of the baseline model.

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Section: Methodsmentioning

confidence: 99%

A Hierarchical Approach for Sentiment Analysis and Categorization of Turkish Written Customer Relationship Management Data

Seyfioğlu¹,

Demirezen²

2017

Annals of Computer Science and Information Systems

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“…[36][37][38][39] finally, text mining is another frequently used approach for educational analytics e.g. [40][41][42][43][44]. Ifenthaler [45][46] have carried out study towards proving that all the upcoming forms of education system will be requiring advanced forms of analytics.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Framework for Big Data Analytics in Education

Shidaganti¹,

Prakash²

2021

IJACSA

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With the adoption of cloud services for hosting knowledge delivery system in educational domain, there is a surplus quantity of education data being generated every day by current learning management system. Such data are associated with certain typical complexities that impose significant challenges for existing database management and analytics. Review of existing approaches towards educational data highlights that they do not offer full-fledged solution towards analytics and still there is an open-end problem. Therefore, the proposed system introduces a comprehensive framework which offers integrated operation of transformation, data quality, and predictive analytics. The emphasis is more towards achieving distributed analytical operation towards educational data in cloud. Implemented using analytical research methodology, the proposed system shows better analytical performance with respect to frequently used educational data analytical approaches.

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“…Unsupervised representation learning approaches for creating word vectors, also referred to as word embeddings, have gained much importance and utilized in several text classification tasks. Word vectors constructed via representation learning approaches such as Word2vec Mikolov, Sutskever, et al, 2013) and GloVe (Pennington et al, 2014) were used in several studies including sentiment analysis (Ouyang et al, 2015), and text mining analysis in Turkish language (Cakir and Guldamlasioglu, 2016).…”

Section: Word Vectorsmentioning

confidence: 99%

MooDetecTR: Kelime Vektörleri Vasıtasıyla Türkçe Şarkı Sözleri için Ruh Hali Tespiti

Cimen

Durahim

2020

Konya Journal of Engineering Sciences

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Along with the increasing use of online music platforms, catalogue-based searches have turned into mood-based seeking. In this study, we propose MooDetecTR, a semi-supervised learning framework that employs word vectors for Turkish song mood detection. In this framework, first word vectors are created through a large collection of textual data, which include more than 2.5 million Turkish documents, by using Word2Vec and GloVe algorithms. Subsequently, lyrics vectors are generated through combining already trained word vectors of the words in the lyrics selected for mood detection. Lastly, lyrics vectors are fed into various machine-learning algorithms as features to create models for music mood detection. For comparison, Turkish music mood detection is performed both via traditional bag-of-words model, with TF-IDF weights, and Doc2Vec algorithm. The effects of stemming of the words and stop-words removal on the results are investigated, as well. The best micro-f1 score (54.36%) obtained by the proposed framework is 3.81%, and 2.92% higher (7.54%, and 5.68% relative improvements) than the best score obtained from Doc2Vec and bag-of-words methods, respectively. Consequently, the results obtained show the effectiveness of incorporating word vectors generated using big textual data into Turkish text classification process, which is clearly illustrated by the improved classification performance.

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Text Mining Analysis in Turkish Language Using Big Data Tools

Cited by 21 publications

References 12 publications

A Hierarchical Approach for Sentiment Analysis and Categorization of Turkish Written Customer Relationship Management Data

A Hierarchical Approach for Sentiment Analysis and Categorization of Turkish Written Customer Relationship Management Data

A Comprehensive Framework for Big Data Analytics in Education

MooDetecTR: Kelime Vektörleri Vasıtasıyla Türkçe Şarkı Sözleri için Ruh Hali Tespiti

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