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LSTM Network based Sentiment Analysis for Customer Reviews

Yıl 2021, Cilt , Sayı , 1 - 1, 31.12.2021
https://doi.org/10.2339/politeknik.844019

Öz

Continuously increasing data bring new problems and problems usually reveal new research areas. One of the new areas is Sentiment Analysis. This field has some difficulties. The fact that people have complex sentiments is the main cause of the difficulty, but this has not prevented the progress of the studies in this field. Sentiment analysis is generally used to obtain information about persons by collecting their texts or expressions. Sentiment analysis can sometimes bring serious benefits. In this study, with singular tag-plural class approach, a binary classification was performed. An LSTM network and several machine learning models were tested. The dataset collected in Turkish, and Stanford Large Movie Reviews datasets were used in this study. Due to the noise in the dataset, the Zemberek NLP Library for Turkic Languages and Regular Expression techniques were used to normalize and clean texts, later, the data were transformed into vector sequences. The preprocessing process made 2% increase to the model performance on the Turkish Customer Reviews dataset. The model was established using an LSTM network. Our model showed better performance than Machine Learning techniques and achieved an accuracy of 90.59% on the Turkish dataset and an accuracy of 89.02% on the IMDB dataset.

Kaynakça

  • [1] Pang B., Lee L. and Vaithyanathan S., “Thumbs up? Sentiment Classification Using Machine Learning Techniques”, Proceedings of EMNLP, 10: 79-86, (2002).
  • [2] Yıldırım S., Salman Y. B. and Ayvaz S., “Türkçe Duygu Kütüphanesi Geliştirme: Sosyal Medya Verileriyle Duygu Analizi Çalışması”, Avrupa Bilim ve Teknoloji Dergisi, 16: 51-60, (2019).
  • [3] Ayvaz S. and Shiha, M. O., “A Scalable Streaming Big Data Architecture for Real-Time Sentiment Analysis”, ICCBDC'18, 47–51, (2018).
  • [4] Brownlee J., “What is Deep Learning?”, Retrieved From: https://machinelearningmastery.com/what-is-deep-learning/, (2019).
  • [5] Amidi A. and Amidi S., “Recurrent Neural Networks cheatsheet”, Retrieved From: https://stanford.edu/~shervine/l/tr/teaching/cs-230/cheatsheet-recurrent-neural-networks, (2019).
  • [6] “Duygu Analizi için Türkçe Veri Seti”, From: https://www.kaggle.com/burhanbilenn/turkish-customer-reviews-for-binary-classification, (2020).
  • [7] Nalçakan Y., Bayramoğlu Ş. and Tuna S., “Sosyal Medya Verileri Üzerinde Yapay Öğrenme ile Duygu Analizi Çalışması”, (2015).
  • [8] Wilson T., Wiebe J. and Hoffmann P., “Recognizing contextual polarity in phrase-level sentiment analysis”, HLT/EMNLP, 05: 347-354, (2005).
  • [9] Dos Santos C. and Gatti de Bayser M., “Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts”, Proceedings of COLING, 25th International Conference on Computational Linguistics: Technical, 69-78, (2014).
  • [10] Ruder S., Ghaffari P. and Breslin J., “A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis”, EMNLP, 999-1005, (2016).
  • [11] Dey L., Chakraborty S., Biswas A., Bose B. and Tiwari S., “Sentiment Analysis of Review Datasets Using Naïve Bayes and K-NN Classifier”, International Journal of Information Engineering and Electronic Business, 8(4): 54-62, (2016).
  • [12] Baid P., Gupta A. and Chaplot N., “Sentiment Analysis of Movie Reviews using Machine Learning Techniques”, International Journal of Computer Applications, 179(7): 45-49, (2017).
  • [13] Singla Z., Randhawa S. and Jain S., “Sentiment analysis of customer product reviews using machine learning”, International Conference on Intelligent Computing and Control (I2C2), 1-5, (2017).
  • [14] Dehkharghani R., Saygin Y., Yanikoglu, B. and Oflazer, Kemal., “SentiTurkNet: a Turkish polarity lexicon for sentiment analysis”, Language Resources and Evaluation, 50: 667-685, (2015).
  • [15] Sağlam F., Genç B. and Sever H., “Extending a sentiment lexicon with synonym–antonym datasets: SWNetTR++”, Turkish Journal of Electrical Engineering and Computer Sciences, 27: 1806-1820, (2019).
  • [16] Kamisli Ozturk Z., Erzurum Cicek Z. and Ergul Aydin Z., “Sentiment Analysis: an Application to Anadolu University”, Acta Physica Polonica A, 132: 753-755, (2017).
  • [17] Wang Y., Huang M., Zhu X. and Zhao L., “Attention-based LSTM for Aspect-level Sentiment Classification”, Proceedings of the Conference on Empirical Methods in Natural Language Processing, 606-615, (2016).
  • [18] Arras L., Montavon G., Müller K., and Samek W., “Explaining Recurrent Neural Network Predictions in Sentiment Analysis”, Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 159-168, (2017).
  • [19] Pant D., Neupane P., Poudel A., Pokhrel A. and Lama B., “Recurrent Neural Network Based Bitcoin Price Prediction by Twitter Sentiment Analysis”, IEEE 3rd International Conference on Computing, Communication and Security (ICCCS), 128-132, (2018).
  • [20] Oğuzlar A., “Veri Ön İşleme”, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21: 67-76, (2003).
  • [21] Akın A. A. and Akın M. D., “Zemberek, an open source nlp framework for Turkic languages”, Structure, 10:1-5, (2007).
  • [22] Go A., Bhayani R. and Huang L., “Twitter sentiment classification using distant supervision”, Processing, 1-6, (2009).
  • [23] Dwarampudi M. and Reddy N. V., “Effects of padding on LSTMs and CNNs”, arXiv, (2019).
  • [24] Tripathy A., Agrawal A. and Rath S., “Classification of Sentimental Reviews Using Machine Learning Techniques”, Procedia Computer Science, 57:821-829, (2015).
  • [25] Maas A., Daly R., Pham P., Huang D., Ng Andrew and Potts C., “Learning Word Vectors for Sentiment Analysis”, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 1:142-150, (2011).
  • [26] CountVectorizer, Scikit-learn, Retrieved From: https://www.scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
  • [27] Text Tokenizer, Keras, Retrieved From: https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text/Tokenizer
  • [28] Embedding Layer, Keras, Retrieved From: https://keras.io/api/layers/core_layers/embedding/

LSTM Network based Sentiment Analysis for Customer Reviews

Yıl 2021, Cilt , Sayı , 1 - 1, 31.12.2021
https://doi.org/10.2339/politeknik.844019

Öz

Continuously increasing data bring new problems and problems usually reveal new research areas. One of the new areas is Sentiment Analysis. This field has some difficulties. The fact that people have complex sentiments is the main cause of the difficulty, but this has not prevented the progress of the studies in this field. Sentiment analysis is generally used to obtain information about persons by collecting their texts or expressions. Sentiment analysis can sometimes bring serious benefits. In this study, with singular tag-plural class approach, a binary classification was performed. An LSTM network and several machine learning models were tested. The dataset collected in Turkish, and Stanford Large Movie Reviews datasets were used in this study. Due to the noise in the dataset, the Zemberek NLP Library for Turkic Languages and Regular Expression techniques were used to normalize and clean texts, later, the data were transformed into vector sequences. The preprocessing process made 2% increase to the model performance on the Turkish Customer Reviews dataset. The model was established using an LSTM network. Our model showed better performance than Machine Learning techniques and achieved an accuracy of 90.59% on the Turkish dataset and an accuracy of 89.02% on the IMDB dataset.

Kaynakça

  • [1] Pang B., Lee L. and Vaithyanathan S., “Thumbs up? Sentiment Classification Using Machine Learning Techniques”, Proceedings of EMNLP, 10: 79-86, (2002).
  • [2] Yıldırım S., Salman Y. B. and Ayvaz S., “Türkçe Duygu Kütüphanesi Geliştirme: Sosyal Medya Verileriyle Duygu Analizi Çalışması”, Avrupa Bilim ve Teknoloji Dergisi, 16: 51-60, (2019).
  • [3] Ayvaz S. and Shiha, M. O., “A Scalable Streaming Big Data Architecture for Real-Time Sentiment Analysis”, ICCBDC'18, 47–51, (2018).
  • [4] Brownlee J., “What is Deep Learning?”, Retrieved From: https://machinelearningmastery.com/what-is-deep-learning/, (2019).
  • [5] Amidi A. and Amidi S., “Recurrent Neural Networks cheatsheet”, Retrieved From: https://stanford.edu/~shervine/l/tr/teaching/cs-230/cheatsheet-recurrent-neural-networks, (2019).
  • [6] “Duygu Analizi için Türkçe Veri Seti”, From: https://www.kaggle.com/burhanbilenn/turkish-customer-reviews-for-binary-classification, (2020).
  • [7] Nalçakan Y., Bayramoğlu Ş. and Tuna S., “Sosyal Medya Verileri Üzerinde Yapay Öğrenme ile Duygu Analizi Çalışması”, (2015).
  • [8] Wilson T., Wiebe J. and Hoffmann P., “Recognizing contextual polarity in phrase-level sentiment analysis”, HLT/EMNLP, 05: 347-354, (2005).
  • [9] Dos Santos C. and Gatti de Bayser M., “Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts”, Proceedings of COLING, 25th International Conference on Computational Linguistics: Technical, 69-78, (2014).
  • [10] Ruder S., Ghaffari P. and Breslin J., “A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis”, EMNLP, 999-1005, (2016).
  • [11] Dey L., Chakraborty S., Biswas A., Bose B. and Tiwari S., “Sentiment Analysis of Review Datasets Using Naïve Bayes and K-NN Classifier”, International Journal of Information Engineering and Electronic Business, 8(4): 54-62, (2016).
  • [12] Baid P., Gupta A. and Chaplot N., “Sentiment Analysis of Movie Reviews using Machine Learning Techniques”, International Journal of Computer Applications, 179(7): 45-49, (2017).
  • [13] Singla Z., Randhawa S. and Jain S., “Sentiment analysis of customer product reviews using machine learning”, International Conference on Intelligent Computing and Control (I2C2), 1-5, (2017).
  • [14] Dehkharghani R., Saygin Y., Yanikoglu, B. and Oflazer, Kemal., “SentiTurkNet: a Turkish polarity lexicon for sentiment analysis”, Language Resources and Evaluation, 50: 667-685, (2015).
  • [15] Sağlam F., Genç B. and Sever H., “Extending a sentiment lexicon with synonym–antonym datasets: SWNetTR++”, Turkish Journal of Electrical Engineering and Computer Sciences, 27: 1806-1820, (2019).
  • [16] Kamisli Ozturk Z., Erzurum Cicek Z. and Ergul Aydin Z., “Sentiment Analysis: an Application to Anadolu University”, Acta Physica Polonica A, 132: 753-755, (2017).
  • [17] Wang Y., Huang M., Zhu X. and Zhao L., “Attention-based LSTM for Aspect-level Sentiment Classification”, Proceedings of the Conference on Empirical Methods in Natural Language Processing, 606-615, (2016).
  • [18] Arras L., Montavon G., Müller K., and Samek W., “Explaining Recurrent Neural Network Predictions in Sentiment Analysis”, Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 159-168, (2017).
  • [19] Pant D., Neupane P., Poudel A., Pokhrel A. and Lama B., “Recurrent Neural Network Based Bitcoin Price Prediction by Twitter Sentiment Analysis”, IEEE 3rd International Conference on Computing, Communication and Security (ICCCS), 128-132, (2018).
  • [20] Oğuzlar A., “Veri Ön İşleme”, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21: 67-76, (2003).
  • [21] Akın A. A. and Akın M. D., “Zemberek, an open source nlp framework for Turkic languages”, Structure, 10:1-5, (2007).
  • [22] Go A., Bhayani R. and Huang L., “Twitter sentiment classification using distant supervision”, Processing, 1-6, (2009).
  • [23] Dwarampudi M. and Reddy N. V., “Effects of padding on LSTMs and CNNs”, arXiv, (2019).
  • [24] Tripathy A., Agrawal A. and Rath S., “Classification of Sentimental Reviews Using Machine Learning Techniques”, Procedia Computer Science, 57:821-829, (2015).
  • [25] Maas A., Daly R., Pham P., Huang D., Ng Andrew and Potts C., “Learning Word Vectors for Sentiment Analysis”, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 1:142-150, (2011).
  • [26] CountVectorizer, Scikit-learn, Retrieved From: https://www.scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
  • [27] Text Tokenizer, Keras, Retrieved From: https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text/Tokenizer
  • [28] Embedding Layer, Keras, Retrieved From: https://keras.io/api/layers/core_layers/embedding/

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Burhan BİLEN
KIRIKKALE ÜNİVERSİTESİ
0000-0002-3106-7369
Türkiye


Fahrettin HORASAN (Sorumlu Yazar)
Dr., Kırıkkale University,
0000-0003-4554-9083
Türkiye

Yayımlanma Tarihi 31 Aralık 2021
Başvuru Tarihi 21 Aralık 2020
Yayınlandığı Sayı Yıl 2021, Cilt , Sayı

Kaynak Göster

Bibtex @araştırma makalesi { politeknik844019, journal = {Politeknik Dergisi}, issn = {}, eissn = {2147-9429}, address = {Gazi Üniversitesi Teknoloji Fakültesi 06500 Teknikokullar - ANKARA}, publisher = {Gazi Üniversitesi}, year = {2021}, volume = {}, pages = {1 - 1}, doi = {10.2339/politeknik.844019}, title = {LSTM Network based Sentiment Analysis for Customer Reviews}, key = {cite}, author = {Bilen, Burhan and Horasan, Fahrettin} }
APA Bilen, B. & Horasan, F. (2021). LSTM Network based Sentiment Analysis for Customer Reviews . Politeknik Dergisi , , 1-1 . DOI: 10.2339/politeknik.844019
MLA Bilen, B. , Horasan, F. "LSTM Network based Sentiment Analysis for Customer Reviews" . Politeknik Dergisi (2021 ): 1-1 <https://dergipark.org.tr/tr/pub/politeknik/issue/33364/844019>
Chicago Bilen, B. , Horasan, F. "LSTM Network based Sentiment Analysis for Customer Reviews". Politeknik Dergisi (2021 ): 1-1
RIS TY - JOUR T1 - LSTM Network based Sentiment Analysis for Customer Reviews AU - Burhan Bilen , Fahrettin Horasan Y1 - 2021 PY - 2021 N1 - doi: 10.2339/politeknik.844019 DO - 10.2339/politeknik.844019 T2 - Politeknik Dergisi JF - Journal JO - JOR SP - 1 EP - 1 VL - IS - SN - -2147-9429 M3 - doi: 10.2339/politeknik.844019 UR - https://doi.org/10.2339/politeknik.844019 Y2 - 2021 ER -
EndNote %0 Politeknik Dergisi LSTM Network based Sentiment Analysis for Customer Reviews %A Burhan Bilen , Fahrettin Horasan %T LSTM Network based Sentiment Analysis for Customer Reviews %D 2021 %J Politeknik Dergisi %P -2147-9429 %V %N %R doi: 10.2339/politeknik.844019 %U 10.2339/politeknik.844019
ISNAD Bilen, Burhan , Horasan, Fahrettin . "LSTM Network based Sentiment Analysis for Customer Reviews". Politeknik Dergisi / (Aralık 2021): 1-1 . https://doi.org/10.2339/politeknik.844019
AMA Bilen B. , Horasan F. LSTM Network based Sentiment Analysis for Customer Reviews. Politeknik Dergisi. 2021; 1-1.
Vancouver Bilen B. , Horasan F. LSTM Network based Sentiment Analysis for Customer Reviews. Politeknik Dergisi. 2021; 1-1.
IEEE B. Bilen ve F. Horasan , "LSTM Network based Sentiment Analysis for Customer Reviews", Politeknik Dergisi, ss. 1-1, Ara. 2022, doi:10.2339/politeknik.844019
 
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