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Proceedings of International Conference on Recent Advancement on Computer and Communication pp 441–447Cite as

Sentiment Analysis Using Lexicon and Machine Learning-Based Approaches: A Survey

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 34))

Abstract

Sentiment analysis is the process of automatic identification of people’s orientation toward individuals, products, services, issues, and events. Task of sentiment analysis requires mining of textual data through natural language processing (NLP). Text method of communication like tweets blog is necessary to examine the emotion of user by studying the input text. Sentiment analysis of social networking sites is a way to identify the user’s opinion. Determination of opinion and strength of the sentiment of user toward entity is growing need of current times. In this paper, a survey on sentiment analysis is done. Text reviews, techniques, lexicon, and machine learning approaches are discussed.

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References

  1. Rodriguez, C., Grivolla, J., Codina, J.: A hybrid framework for scalable opinion Mining in Social Media. In: Proceeding of the Workshop on Semantic Analysis in Social Media (2012)

    Google Scholar 

  2. Priya Mohana, R.: Sentiment Analysis and Opinion mining using SentiwordNet: A Survey (2015)

    Google Scholar 

  3. Hanen, A., Salma, J.: Dynamic construction of dictionaries for sentiment classification. In: Third International Conference on Cloud and Green Computing (CGC), Karlsruhe (2013)

    Google Scholar 

  4. Augustniak, L., Kajdanowicz,T., et al.: Simple is better? Lexicon based ensemble sentiment classification beats supervised methods. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Beijing (2014)

    Google Scholar 

  5. Dang, Y., Zhang, Y., Chen, H.: A lexicon-enhanced method for sentiment classification: an experiment on online product reviews. IEEE Intell. Syst. 25(4), 46–53 (2009)

    Article  Google Scholar 

  6. Goodarzi, M., Mahmoudi, M., Zamani, R.: A framework for sentiment analysis on schema-based research content via lexical analysis. In: 7th International Symposium on Telecommunications (IST), Tehran (2014)

    Google Scholar 

  7. Liu, B., Blasch, E., Chen, Y., Shen, D.: Scalable sentiment classification for big data analysis using Naïve Bayes Classifier. In: IEEE International Conference on Big Data, Silicon Valley, CA (2013)

    Google Scholar 

  8. Yongzehng, Z., Dan, S., Catherine, B.: Sentiment analysis in Practice, Dec (2011)

    Google Scholar 

  9. Zhou, X., Tao, X., Yong, J., Yang, Z.: Sentiment analysis on tweets for social events. In: Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (2013)

    Google Scholar 

  10. Bing, L., Lei, Z.: A Survey of Opinion Mining and Sentiment Analysis. Springer, pp. 415–463 (2012)

    Google Scholar 

  11. Hailong, Z., Wenyan, G., Bo, J.: Machine Learning and Lexicon Based Methods for Sentiment Classification, IEEE, vol. 11, pp. 262–265 (2014)

    Google Scholar 

  12. Walaa, M., Ahmed, H., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2013)

    Google Scholar 

  13. Baharum, B., Lam, H.L., Khairullah, K.: A review of machine learning algorithms for text-documents classification. J. Adv. Inf. Technol. 1(1), 4–20 (2010)

    Google Scholar 

  14. Wan, X.: Using Bilingual knowledge and ensemble technique for unsupervised Chinese sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 553–561 (2008)

    Google Scholar 

  15. Hardeniya, T., Borikar, D.A.: Dictionary based approach to sentiment analysis—a review. In: IJAEMS: Open Access International Journal, vol. 2(5). Infogain Publication (2016)

    Google Scholar 

  16. Ye, Q., Zhang, Z., Law, R.: Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst. Appl. 36, 6527–6535 (2009). https://doi.org/10.1016/j.eswa.2008.07.035

    Article  Google Scholar 

  17. Sindhwani, V, Melville, P.: Document-word co-regularization for semi-supervised sentiment analysis. In: Eight IEEE International Conference on Data Mining, 2008.ICDM 08, pp. 1025–1030. IEEE (2008)

    Google Scholar 

  18. D’Andrea, A., Ferri, F., Grifoni, P., Guzzo, T.: Approaches, tools and applications for sentiment analysis implementation. Int. J. Comput. Appl. (0975-8887) 125(3) (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. BU, Bhopal, India

    Binita Verma

  2. Department of Computer Application and Mathematics, Maulana Azad National Intitute of Technology, Bhopal, Madhya Pradesh, India

    Ramjeevan Singh Thakur

Authors
  1. Binita Verma
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  2. Ramjeevan Singh Thakur
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Corresponding author

Correspondence to Binita Verma .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Technocrats Institute of Technology, Bhopal, Madhya Pradesh, India

    Basant Tiwari

  2. Computer Science and Engineering, DSPM IIIT, Naya Raipur, Chhattisgarh, India

    Vivek Tiwari

  3. Department of Mathematics, Sungkyunkwan University, Suwon, Korea (Republic of)

    Kinkar Chandra Das

  4. Microsoft Innovation Academy, Computer Science and Engineering, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

  5. Department of Mathematics, South Asian University, New Delhi, Delhi, India

    Jagdish C. Bansal

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Verma, B., Thakur, R.S. (2018). Sentiment Analysis Using Lexicon and Machine Learning-Based Approaches: A Survey. In: Tiwari, B., Tiwari, V., Das, K., Mishra, D., Bansal, J. (eds) Proceedings of International Conference on Recent Advancement on Computer and Communication . Lecture Notes in Networks and Systems, vol 34. Springer, Singapore. https://doi.org/10.1007/978-981-10-8198-9_46

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  • DOI: https://doi.org/10.1007/978-981-10-8198-9_46

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8197-2

  • Online ISBN: 978-981-10-8198-9

  • eBook Packages: EngineeringEngineering (R0)

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