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Sentiment Analysis Using Text Mining: A Review

Received: 25 June 2018     Published: 26 June 2018
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Abstract

Text mining and sentiment analysis have received huge attention recently, specially because of the availability of vast data in form of text available on social media, e-commerce websites, blogs and other similar sources. This data is usually unstructured and contains noise, therefore the task of gaining information is complex and expensive. There is a growing need for developing different methodologies and models for efficiently processing the texts and extracting apt information. One way to extract information is text mining and sentiment analysis, that include: data acquisition, data pre-processing and normalization, feature extraction and representation, labelling, and finally the application of various Natural Language Processing (NLP) and machine learning algorithms. This paper provides an overview of different methods used in text mining and sentiment analysis elaborating on all subtasks.

Published in International Journal on Data Science and Technology (Volume 4, Issue 2)
DOI 10.11648/j.ijdst.20180402.12
Page(s) 49-53
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Sentiment Analysis, Supervised Learning, Unsupervised Learning, Text Mining, Feature Extraction, Feature Representation

References
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[9] Wang, S., & Manning, C. D. (2012, July). Baselines and bigrams: Simple, good sentiment and topic classification. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2 (pp. 90-94). Association for Computational Linguistics.
[10] Ng, V., Dasgupta, S., & Arifin, S. M. (2006, July). Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews. In Proceedings of the COLING/ACL on Main conference poster sessions (pp. 611-618). Association for Computational Linguistics.
[11] Mesnil, G., Mikolov, T., Ranzato, M. A., & Bengio, Y. (2014). Ensemble of generative and discriminative techniques for sentiment analysis of movie reviews. arXiv preprint arXiv:1412.5335.
[12] Tripathy, A., Agrawal, A., & Rath, S. K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57, 117-126.
[13] Zheng, L., Wang, H., & Gao, S. (2018). Sentimental feature selection for sentiment analysis of Chinese online reviews. International journal of machine learning and cybernetics, 9 (1), 75-84.
[14] Law, D., Gruss, R., & Abrahams, A. S. (2017). Automated defect discovery for dishwasher appliances from online consumer reviews. Expert Systems with Applications, 67, 84-94.
[15] Nguyen, D. Q., Nguyen, D. Q., Vu, T., & Pham, S. B. (2014). Sentiment classification on polarity reviews: an empirical study using rating-based features. In Proceedings of the 5th workshop on computational approaches to subjectivity, sentiment and social media analysis (pp. 128-135).
[16] Tiwari, P., Mishra, B. K., Kumar, S., & Kumar, V. (2017). Implementation of n-gram methodology for rotten tomatoes review dataset sentiment analysis. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 7 (1), 30-41.
[17] Arun, K., Srinagesh, A., & Ramesh, M. (2017). Twitter Sentiment Analysis on Demonetization tweets in India Using R language. International Journal of Computer Engineering in Research Trends, 4 (6), 252-258.
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  • APA Style

    Swati Redhu, Sangeet Srivastava, Barkha Bansal, Gaurav Gupta. (2018). Sentiment Analysis Using Text Mining: A Review. International Journal on Data Science and Technology, 4(2), 49-53. https://doi.org/10.11648/j.ijdst.20180402.12

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    ACS Style

    Swati Redhu; Sangeet Srivastava; Barkha Bansal; Gaurav Gupta. Sentiment Analysis Using Text Mining: A Review. Int. J. Data Sci. Technol. 2018, 4(2), 49-53. doi: 10.11648/j.ijdst.20180402.12

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    AMA Style

    Swati Redhu, Sangeet Srivastava, Barkha Bansal, Gaurav Gupta. Sentiment Analysis Using Text Mining: A Review. Int J Data Sci Technol. 2018;4(2):49-53. doi: 10.11648/j.ijdst.20180402.12

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  • @article{10.11648/j.ijdst.20180402.12,
      author = {Swati Redhu and Sangeet Srivastava and Barkha Bansal and Gaurav Gupta},
      title = {Sentiment Analysis Using Text Mining: A Review},
      journal = {International Journal on Data Science and Technology},
      volume = {4},
      number = {2},
      pages = {49-53},
      doi = {10.11648/j.ijdst.20180402.12},
      url = {https://doi.org/10.11648/j.ijdst.20180402.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20180402.12},
      abstract = {Text mining and sentiment analysis have received huge attention recently, specially because of the availability of vast data in form of text available on social media, e-commerce websites, blogs and other similar sources. This data is usually unstructured and contains noise, therefore the task of gaining information is complex and expensive. There is a growing need for developing different methodologies and models for efficiently processing the texts and extracting apt information. One way to extract information is text mining and sentiment analysis, that include: data acquisition, data pre-processing and normalization, feature extraction and representation, labelling, and finally the application of various Natural Language Processing (NLP) and machine learning algorithms. This paper provides an overview of different methods used in text mining and sentiment analysis elaborating on all subtasks.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Sentiment Analysis Using Text Mining: A Review
    AU  - Swati Redhu
    AU  - Sangeet Srivastava
    AU  - Barkha Bansal
    AU  - Gaurav Gupta
    Y1  - 2018/06/26
    PY  - 2018
    N1  - https://doi.org/10.11648/j.ijdst.20180402.12
    DO  - 10.11648/j.ijdst.20180402.12
    T2  - International Journal on Data Science and Technology
    JF  - International Journal on Data Science and Technology
    JO  - International Journal on Data Science and Technology
    SP  - 49
    EP  - 53
    PB  - Science Publishing Group
    SN  - 2472-2235
    UR  - https://doi.org/10.11648/j.ijdst.20180402.12
    AB  - Text mining and sentiment analysis have received huge attention recently, specially because of the availability of vast data in form of text available on social media, e-commerce websites, blogs and other similar sources. This data is usually unstructured and contains noise, therefore the task of gaining information is complex and expensive. There is a growing need for developing different methodologies and models for efficiently processing the texts and extracting apt information. One way to extract information is text mining and sentiment analysis, that include: data acquisition, data pre-processing and normalization, feature extraction and representation, labelling, and finally the application of various Natural Language Processing (NLP) and machine learning algorithms. This paper provides an overview of different methods used in text mining and sentiment analysis elaborating on all subtasks.
    VL  - 4
    IS  - 2
    ER  - 

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Author Information
  • Department of Applied Sciences, The NorthCap University, Gurgaon, India

  • Department of Applied Sciences, The NorthCap University, Gurgaon, India

  • Department of Applied Sciences, The NorthCap University, Gurgaon, India

  • School of Mathematical Sciences, College of Natural, Applied and Health Sciences, Wenzhou-Kean University, Wenzhou, China

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