Sentiment analysis on mobile phone reviews using supervised learning techniques

Автор: Momina Shaheen, Shahid M. Awan, Nisar Hussain, Zaheer A. Gondal

Журнал: International Journal of Modern Education and Computer Science @ijmecs

Статья в выпуске: 7 vol.11, 2019 года.

Бесплатный доступ

Opinion Mining or Sentiment Analysis is the process of mining emotions, attitudes, and opinions automatically from speech, text, and database sources through Natural Language Processing (NLP). Opinions can be given on anything. It may be a product, feature of a product or any sentiment view on a product. In this research, Mobile phone products reviews, fetched from Amazon.com, are mined to predict customer rating of the product based on its user reviews. This is performed by the sentiment classification of unlocked mobile reviews for the sake of opinion mining. Different opinion mining algorithms are used to identify the sentiments hidden in the reviews and comments for a specific unlocked mobile. Moreover, a performance analysis of Sentiment Classification algorithms is performed on the data set of mobile phone reviews. Results yields from this research provide the comparative analysis of eight different classifiers on the evaluation parameters of accuracy, recall, precision and F-measure. The Random Forest Classifiers offers more accurate predictions than others but LSTM and CNN also give better accuracy.

Еще

Sentiment Classification, NLP, Opinion Mining, NB-SVM, Random Forest, LSTM, CNN, Phone Reviews

Короткий адрес: https://sciup.org/15016864

IDR: 15016864   |   DOI: 10.5815/ijmecs.2019.07.04

Список литературы Sentiment analysis on mobile phone reviews using supervised learning techniques

  • R. Sharma, S. Nigam, and R. Jain, “Supervised Opinion Mining Techniques : A Survey,” vol. 3, no. 8, pp. 737–742, 2013.
  • W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Eng. J., vol. 5, no. 4, pp. 1093–1113, 2014.
  • A. Esuli and F. Sebastiani, “SENTIWORDNET: A high-coverage lexical resource for opinion mining,” Evaluation, pp. 1–26, 2007.
  • B. Pang and L. Lee, “A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts,” 2004.
  • M. Hu and B. Liu, “Mining and summarizing customer reviews,” Proc. 2004 ACM SIGKDD Int. Conf. Knowl. Discov. data Min. - KDD ’04, p. 168, 2004.
  • M. Hu and B. Liu, “Mining Opinion Features in Customer Reviews,” 19th Natl. Conf. Artifical Intell., pp. 755–760, 2004.
  • G. A. Miller, R. Beckwith, C. Fellbaum, D. Gross, and K. J. Miller, “Introduction to wordnet: An on-line lexical database,” Int. J. Lexicogr., vol. 3, no. 4, pp. 235–244, 1990.
  • B. Ohana and B. Tierney, “Sentiment classification of reviews using SentiWordNet,” Sch. Comput. 9th. IT T Conf., p. 13, 2009.
  • R. Prabowo and M. Thelwall, “Sentiment analysis: A combined approach,” J. Informetr., vol. 3, no. 2, pp. 143–157, Apr. 2009.
  • K. Ghag and K. Shah, “Comparative analysis of the techniques for Sentiment Analysis,” Int. Conf. Adv. Technol. Eng., no. 124, pp. 1–7, 2013.
  • A. Gupte, S. Joshi, P. Gadgul, and A. Kadam, “Comparative Study of Classification Algorithms used in Sentiment Analysis,” Int. J. Comput. Sci. Inf. Technol., vol. 5, no. 5, pp. 6261–6264, 2014.
  • C. Bhadane, H. Dalal, and H. Doshi, “Sentiment analysis: Measuring opinions,” Procedia Comput. Sci., vol. 45, no. C, pp. 808–814, 2015.
  • E. K. Fi et al., “a Lexicon-Based Text Classification,” 2016.
  • S. Pasarate and R. Shedge, “Comparative study of feature extraction techniques used in sentiment analysis,” 2016 Int. Conf. Innov. Challenges Cyber Secur., no. Iciccs, pp. 182–186, 2016.
  • I. Journal, “Sentiment Classi fi cation using Decision Tree Based Feature Selection,” no. January 2016, 2017.
  • A. J. Singh, “Sentiment Analysis : A Comparative Study of Supervised Machine Learning Algorithms Using Rapid miner,” vol. 5, no. Xi, pp. 80–89, 2017.
  • S. Rana and A. Singh, “Comparative analysis of sentiment orientation using SVM and Naive Bayes techniques,” in 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), 2016, pp. 106–111.
  • L. Breiman, “Random Forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
  • Vladimir Svetnik, Andy Liaw, Christopher Tong, J. Christopher Culberson, and Robert P. Sheridan, and B. P. Feuston, “Random Forest:  A Classification and Regression Tool for Compound Classification and QSAR Modeling,” 2003.
  • G. IZMIRLIAN, “Application of the Random Forest Classification Algorithm to a SELDI-TOF Proteomics Study in the Setting of a Cancer Prevention Trial,” Ann. N. Y. Acad. Sci., vol. 1020, no. 1, pp. 154–174, May 2004.
  • M. Mursalin, Y. Zhang, Y. Chen, and N. V Chawla, “Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier,” Neurocomputing, vol. 241, pp. 204–214, Jun. 2017.
  • C. W. Jian, M. Z. Ibrahim, W. Thum, T. Seong, W. Ei, and S. Khatun, “Embedded Character Recognition System using Random Forest Algorithm for IC Inspection System.”
  • S. Joshi, H. Upadhyay, L. Lagos, N. S. Akkipeddi, and V. Guerra, “Machine Learning Approach for Malware Detection Using Random Forest Classifier on Process List Data Structure,” in Proceedings of the 2nd International Conference on Information System and Data Mining - ICISDM ’18, 2018, pp. 98–102.
  • N. Dogru and A. Subasi, “Traffic accident detection using random forest classifier,” in 2018 15th Learning and Technology Conference (L&T), 2018, pp. 40–45.
  • L. Saitta, R. E. European Coordinating Committee for Artificial Intelligence., and Associazione italiana per l’intelligenza artificiale., Machine learning : proceedings of the Thirteenth International Conference (ICML ’96). Morgan Kaufmann Publishers, 1996.
  • C. Muramatsu, S. Higuchi, H. Fujita, T. Morita, and M. Oiwa, “Similarity estimation for reference image retrieval in mammograms using convolutional neural network,” in Medical Imaging 2018: Computer-Aided Diagnosis, 2018, vol. 10575, p. 101.
  • U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, H. Adeli, and D. P. Subha, “Automated EEG-based screening of depression using deep convolutional neural network,” Comput. Methods Programs Biomed., vol. 161, pp. 103–113, Jul. 2018.
  • J. Hyeon and Y. M. Communications Business, “Large-scale Video Classification guided by Batch Normalized LSTM Translator.”
  • S. Merity, N. Shirish Keskar, and R. Socher, “Regularizing and Optimizing LSTM Language Models,” 2017.
  • A. Zeyer, P. Doetsch, P. Voigtlaender, R. Schlüter, and H. Ney, “A COMPREHENSIVE STUDY OF DEEP BIDIRECTIONAL LSTM RNNS FOR ACOUSTIC MODELING IN SPEECH RECOGNITION.”
  • S. K. Gonugondla, M. Kang, and N. Shanbhag, “A 42pJ/decision 3.12TOPS/W robust in-memory machine learning classifier with on-chip training,” Dig. Tech. Pap. - IEEE Int. Solid-State Circuits Conf., vol. 61, pp. 490–492, 2018.
Еще
Статья научная