Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6088
Title: A Hierarchical Approach for Sentiment Analysis and Categorization of Turkish Written Customer Relationship Management Data
Authors: Seyfioğlu, Mehmet Saygın
Demirezen, Mustafa Umut
Keywords: customer relationship management
word2vec
doc2vec
classification
sentiment analysis
xgboost
Issue Date: 2017
Publisher: IEEE
Source: Federated Conference on Computer Science and Information Systems (FedCSIS) -- SEP 03-06, 2017 -- Prague, CZECH REPUBLIC
Series/Report no.: Federated Conference on Computer Science and Information Systems
Abstract: 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.
URI: https://doi.org/10.15439/2017F204
https://hdl.handle.net/20.500.11851/6088
ISBN: 978-8-3946-2537-5
ISSN: 2325-0348
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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