Emotion detection on cross platform languages

  • Authors

    • Mohammad Arif Student at Department of CSE, chandigarh university
    • Mandeep Singh Department of CSE, chandigarh university
    • Rajdavinder Boparai Department of CSE, chandigarh university
    2018-06-05
    https://doi.org/10.14419/ijet.v7i2.27.10783
  • Cross Platform Language, Emotion Detection, Social Networks, Subjective Lexicon-Based Approach.

  • Abstract

    Internet has changed the course of our living. It has become the most beneficial antecedent or source of information. Today almost everything is found on internet. Everyday millions of people post their ideas, reviews, stories about the services, products or other persons. The size of data is increasing tremendously. It is very difficult to analyze that amount of data and figure out the emotions or sentiments posed by people. Emotion detection is such a technique where we can judge people’s ideas and extract the emotion towards an entity or service. We have used subjective lexicon-based approach to bench the emoticons expressed by the ideas of the people. The data set that we have mainly focused is very cross and noisy. We have used Facebook data in Urdu and Kashmiri language. Both languages are very cross domain. These languages can be written in English alphabet that makes them more challenging to analyses. Our approach resolves the challenge to the maximum possible way. The results shown by our method on this kind of data set are better than any other approach. Our analysis on this type of dataset will help the local businessmen of these areas to grow and flourish. The analysis will give some insights what the local feel about the entity or product so that the manufacturers can design or build it that way and try to enhance its qualities.

     

     

  • References

    1. [1] N. Pannala, C. Nawarathna, J. Jayakody, L. Rupasinghe and K. Krishnadeva, "Supervised Learning Based Approach to Aspect Based Sentiment Analysis", 2016 IEEE International Conference on Computer and Information Technology (CIT), 2016. https://doi.org/10.1109/CIT.2016.107.

      [2] Chenghua Lin, Yulan He, Richard Everson, Member, IEEE, and Stefan Ru¨ger,†Weakly Supervised Joint Sentiment-Topic Detection from Textâ€, IEEE trans. on knowledge and data engineering, vol. 24, no. 6, June 2012. https://doi.org/10.1109/TKDE.2011.48.

      [3] M. Paredes-Valverde, R. Colomo-Palacios, M. Salas-Zárate and R. Valencia-García, "Sentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach", Scientific Programming, vol. 2017, pp. 1-6, 2017. https://doi.org/10.1155/2017/1329281.

      [4] Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In:Proceedings of the conference on human language technology and empirical methods in natural language processing. Association for Computational Linguistics, Stroudsburg, PA, USA. https://doi.org/10.3115/1220575.1220619.

      [5] Muhammaad Zubair, Aurangzeb Khan, Shakeel Ahmad, FazalMasudKundi and Asghar, 2014.A Review of Feature Extraction in Sentiment Analysis. ISSN 2090-4304 Journal of Basic and Applied Scientific Research.

      [6] M. Al-Kabi, A. Gigieh, I. Alsmadi, H. Wahsheh, and M. Haidar, "An Opinion Analysis Tool for Colloquial and Standard Arabic," In The fourth International Conference on Information and Communication Systems (ICICS 2013), 2013.

      [7] Moreo A, Romero M, Castro JL, Zurita JM. “Lexicon-based commentsoriented news sentiment analyzer system†Expert Syst Appl, 39:9166–80, 2012. https://doi.org/10.1016/j.eswa.2012.02.057.

      [8] H Saif, M Fernández, Y He, H Alani (2014),On stopwords, filtering and data sparsity for sentiment analysis of Twitter.

      [9] Kang Hanhoon, Yoo Seong Joon, Han Dongil., “Senti-lexicon and improved Naı¨ve Bayes algorithms for sentiment analysis of restaurant reviewsâ€, Expert Syst Appl, 39:6000–10, 2012. https://doi.org/10.1016/j.eswa.2011.11.107.

      [10] V. M. Pradhan, J. Vala, and P. Balani, “A Survey on Sentiment Analysis Algorithms for Opinion Mining,†Int. J. Comput. Appl., vol. 133, no. 9, 2016.

      [11] K. Ahmed, N. El Tazi, and A. H. Hossny, “Sentiment Analysis over Social Networks: An Overview,†in 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2015, no. October. https://doi.org/10.1109/SMC.2015.380.

      [12] A Semantic web based filtering techniques through web service recommendation", International Journal of Engineering & Technology, vol. 7, no. 2-7, p. 41, 2018.

      [13] T. V.R. Sai, S. Haaris and S. Sridevi, "Website evaluation using opinion mining", International Journal of Engineering & Technology, vol. 7, no. 2-7, p. 51, 2018. https://doi.org/10.14419/ijet.v7i2.7.10257.

      [14] “A Method for finding threated web sites through crime data mining and sentiment analysis", International Journal of Engineering & Technology, vol. 7, no. 2-7, p. 62, 2018.

      [15] User behavior analysis on agriculture mining system", International Journal of Engineering & Technology, vol. 7, no. 2-7, p. 37, 2018.

      [16] V. Ramya and K. Rao, "Sentiment Analysis of Movie Review using Machine Learning Techniques", vol. &, no. 7, 2018.

      [17] Xin Chen,Mihaela Vorvoreanu, and Krishna Madhavan,†Mining Social Media Data for Understanding Students’ Learning Experiences†IEEE trans. on learning technologies, vol. 7, no. 3, JulySeptember 2014. https://doi.org/10.1109/TLT.2013.2296520.

      [18] Danushka Bollegala, David Weir, and John Carroll,†Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurusâ€, IEEE trans. on knowledge and data engineering, vol. 25, no. 8, August 2013 https://doi.org/10.1109/TKDE.2012.103.

      [19] Xiaohui Yu,Yang Liu, Jimmy Xiangji Huang, and Aijun An,,†Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domainâ€, IEEE Trans.On knowledge and data engineering, vol. 24, no. 4, April 2012 https://doi.org/10.1109/TKDE.2010.269.

      [20] G. Salton, “Automatic Text Processing: The Transformation, Analysis and Retrieval of Information by Computerâ€, Addison-Wesley, 1989.

      [21] Carlo Strapparava, Rada Mihalcea, “Learning to Identify Emotions in Textâ€, SAC’08 Fortaleza, Brazil 2008 https://doi.org/10.1145/1363686.1364052.

      [22] Wiebe, J., “Learning subjective adjectives from corporaâ€, Proceedings of the 17th National Conference on Artificial Intelligence (AAAI-2000), Austin, Texas, 2000

      [23] A. Kathuria and S. Upadhay, "International Journal of Computer Science and Mobile Computing", International Journal of Computer Science and Mobile Computing, vol. 6, no. 4, pp. 17-22, 2017.

      [24] M. Gaur and J. Pruthi, "A Survey on Sentiment Analysis and Opinion Mining", international journal of current engineering and technology, vol. 7, no. 2, 2017.

      [25] Z. Nanli, Z. Ping, L. Weiguo and C. Meng, "Sentiment analysis: A literature review", 2012 International Symposium on Management of Technology (ISMOT), 2012. https://doi.org/10.1109/ISMOT.2012.6679538.

      [26] T. Shivaprasad and J. Shetty, "Sentiment analysis of product reviews: A review", 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), 2017. https://doi.org/10.1109/ICICCT.2017.7975207.

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  • How to Cite

    Arif, M., Singh, M., & Boparai, R. (2018). Emotion detection on cross platform languages. International Journal of Engineering & Technology, 7(2.27), 27-31. https://doi.org/10.14419/ijet.v7i2.27.10783

    Received date: 2018-03-29

    Accepted date: 2018-04-16

    Published date: 2018-06-05