Perspectives of the performance metrics in lexicon and hybrid based approaches: a review

  • Authors

    • Meesala Shobha Rani Associate ProfessorSchool of Information Technology and engineeringVIT University
    • Sumathy S
    2017-09-26
    https://doi.org/10.14419/ijet.v6i4.8295
  • , Corpus Based Approach, Dictionary Based Approach, Hybrid Based Approach, Lexicon Based Approach, Sentiment Analysis, Text Mining.

  • Abstract

    Online social media and social networking services experience a drastic development in the present scenario. Contents generated by hundreds of millions of users are used for communication in general. Users mark their opinion and review in various applications such as Twitter, Facebook, YouTube, Weibo, Flicker, LinkedIn, Online-e commerce sites, Microblogging sites, etc. User generated text is spread rapidly on the web, and it has become tedious to analyze the opinionated text in order to arrive at a decision. Sentiment analysis, a sub-category of text mining is the major active research domain in current era due to greater quantity of opinionated text present in the Internet. Semantic detection is the sub-class in the sentiment analysis which is used for measuring the sentiment orientation in any text. Opinionated text is used for analyzing and making the decision simple. This interdisciplinary field draws various techniques from data mining, machine learning, natural language processing, lexicon based and hybrid based approaches. This paper provides a broad perspective with the highlight of the current state-of art techniques emphasizing the various research challenges and gaps present. The performance metrics in terms of detection rate, precision, recall, f-measure/score, average mean, auto-Pearson correlation, cosine similarity and ratio of time on various algorithms is discussed in detail. An analysis of the text mining approaches in different domains is presented.

  • References

    1. [1] S. Sun, C. Luo, and J. Chen, “A review of natural language processing techniques for opinion mining systems,†Inf. Fusion, 2017. https://doi.org/10.1016/j.inffus.2016.10.004.

      [2] F. Khan, U. Qamar, and S. Bashir, “Lexicon based semantic detection of sentiments using expected likelihood estimate smoothed odds ratio,†Artif. Intell. Rev., 2017. https://doi.org/10.1007/s10462-016-9496-4.

      [3] B. Liu, “Sentiment analysis and opinion mining,†Synth. Lect. Hum. Lang. Technol., 2012.

      [4] K. Ravi and V. Ravi, “A survey on opinion mining and sentiment analysis: tasks, approaches and applications,†Knowledge-Based Syst., 2015.

      [5] C. Li, J. Yang, and S. Park, “Text categorization algorithms using semantic approaches, corpus-based thesaurus and WordNet,†Expert Syst. Appl., 2012. https://doi.org/10.1016/j.eswa.2011.07.070.

      [6] M. Fernández-Gavilanes and T. Ãlvarez-López, “Unsupervised method for sentiment analysis in online texts,†Expert Syst. with, 2016. https://doi.org/10.1016/j.eswa.2016.03.031.

      [7] R. Shah, Y. Yu, A. Verma, S. Tang, and A. Shaikh, “Leveraging multimodal information for event summarization and concept-level sentiment analysis,†Knowledge-Based, 2016.

      [8] D. Tang, F. Wei, B. Qin, N. Yang, T. Liu, and M. Zhou, “Sentiment Embeddings with Applications to Sentiment Analysis,†IEEE Trans. Knowl. Data Eng., vol. 28, no. 2, pp. 496–509, Feb. 2016. https://doi.org/10.1109/TKDE.2015.2489653.

      [9] J. Lochter, R. Zanetti, D. Reller, and T. Almeida, “Short text opinion detection using ensemble of classifiers and semantic indexing,†Expert Syst. with, 2016. https://doi.org/10.1016/j.eswa.2016.06.025.

      [10] Z. Xu, X. Luo, S. Zhang, X. Wei, L. Mei, and C. Hu, “Mining temporal explicit and implicit semantic relations between entities using web search engines,†Futur. Gener. Comput., 2014.

      [11] H. Saif, Y. He, M. Fernandez, and H. Alani, “Contextual semantics for sentiment analysis of Twitter,†Process. Manag., 2016.

      [12] Y. Gutiérrez, S. Vázquez, and A. Montoyo, “A semantic framework for textual data enrichment,†Expert Syst. Appl., 2016. https://doi.org/10.1016/j.eswa.2016.03.048.

      [13] A. Felbermayr and A. Nanopoulos, “The Role of Emotions for the Perceived Usefulness in Online Customer Reviews,†J. Interact. Mark., vol. 36, pp. 60–76, Nov. 2016. https://doi.org/10.1016/j.intmar.2016.05.004.

      [14] S. Chakraverty and M. Saraswat, “Review based emotion profiles for cross domain recommendation,†Multimed. Tools Appl., 2017.

      [15] Y. Wang, Y. Rao, X. Zhan, H. Chen, and M. Luo, “Sentiment and emotion classification over noisy labels,†Knowledge-Based Syst., 2016.

      [16] E. Iglesias, A. Vieira, and L. Borrajo, “An HMM-based over-sampling technique to improve text classification,†Expert Syst. Appl., 2013. https://doi.org/10.1016/j.eswa.2013.07.036.

      [17] Z. Lin, X. Jin, X. Xu, Y. Wang, and X. Cheng, “An unsupervised cross-lingual topic model framework for sentiment classification,†Audio, Speech, …, 2016.

      [18] H. J. Escalante et al., “Term-weighting learning via genetic programming for text classification,†Knowledge-Based Syst., vol. 83, pp. 176–189, Jul. 2015. https://doi.org/10.1016/j.knosys.2015.03.025.

      [19] Y. Rao, H. Xie, J. Li, F. Jin, F. Wang, and Q. Li, “Social emotion classification of short text via topic-level maximum entropy model,†Inf. Manag., 2016. https://doi.org/10.1016/j.im.2016.04.005.

      [20] O. Vechtomova, “Disambiguating context-dependent polarity of words: An information retrieval approach,†Inf. Process. Manag., 2017. https://doi.org/10.1016/j.ipm.2017.03.007.

      [21] M. Asghar, S. Ahmad, M. Qasim, S. Zahra, and F. Kundi, “SentiHealth: creating health-related sentiment lexicon using hybrid approach,†Springerplus, 2016. https://doi.org/10.1186/s40064-016-2809-x.

      [22] P. Wang, B. Xu, J. Xu, G. Tian, C. Liu, and H. Hao, “Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification,†Neurocomputing, 2016. https://doi.org/10.1016/j.neucom.2015.09.096.

      [23] C. Hung and S. Chen, “Word sense disambiguation based sentiment lexicons for sentiment classification,†Knowledge-Based Syst., 2016.

      [24] O. Appel, F. Chiclana, J. Carter, and H. Fujita, “A hybrid approach to the sentiment analysis problem at the sentence level,†Knowledge-Based Syst., vol. 108, pp. 110–124, Sep. 2016. https://doi.org/10.1016/j.knosys.2016.05.040.

      [25] S. Poria, E. Cambria, and A. Gelbukh, “Aspect extraction for opinion mining with a deep convolutional neural network,†Knowledge-Based Syst., 2016.

      [26] M. Thelwall, “TensiStrength: Stress and relaxation magnitude detection for social media texts,†Inf. Process. Manag., 2017. https://doi.org/10.1016/j.ipm.2016.06.009.

      [27] H. Keshavarz and M. S. Abadeh, “ALGA: Adaptive lexicon learning using genetic algorithm for sentiment analysis of microblogs,†Knowledge-Based Syst., vol. 122, pp. 1–16, Apr. 2017. https://doi.org/10.1016/j.knosys.2017.01.028.

      [28] F. Khan, U. Qamar, and S. Bashir, “eSAP: A decision support framework for enhanced sentiment analysis and polarity classification,†Inf. Sci. (Ny)., 2016.

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  • How to Cite

    Rani, M. S., & S, S. (2017). Perspectives of the performance metrics in lexicon and hybrid based approaches: a review. International Journal of Engineering & Technology, 6(4), 108-115. https://doi.org/10.14419/ijet.v6i4.8295

    Received date: 2017-08-29

    Accepted date: 2017-09-19

    Published date: 2017-09-26