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Sentence Level Sentiment Identification and Calculation from News Articles Using Machine Learning Techniques

  • Vishal S. Shirsat
  • Rajkumar S. Jagdale
  • Sachin N. Deshmukh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)

Abstract

Sentiment analysis is a widely used phenomenon for analyzing online user responses to infer collective response and it is used in various applications. Negation is a very common morphological creation that affects polarity. This research paper focuses on sentence level negation identification from news articles this work uses online news articles Data from BBC news. Results are analyzed using Machine Learning Algorithms like Support vector Machine and Naïve Bayes. Support Vector Machine achieves 96.46% accuracy and Naive Bayes achieves 94.16%.

Keywords

Sentiment analysis Support vector machine Naïve Bayes Machine learning algorithm Negation identification 

References

  1. 1.
    Roebuck, K.: Sentiment Analysis: High-Impact Strategies What You Need to Now: Definitions, Adoptions, Impact, Benefits, Maturity. Vendors, Emereo Publishing, 05 Nov 2012Google Scholar
  2. 2.
    Pooja, P., Sharvari, G.: A survey of sentiment classification techniques used for indian regional languages. Int. J. Comput. Sci. Appl. 5(2) April 2015Google Scholar
  3. 3.
    Bo, P., Lillian, L., Shivakumar, V.: Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (2002)Google Scholar
  4. 4.
    Mohammad, S., Dorr, B., Dunne, C.: Generating high-coverage semantic orientation Lexicons from overly marked words and a thesaurus. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp. 599–608 (2009)Google Scholar
  5. 5.
    Turney, P.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the Association for Computational Linguistics, pp. 417–424, Philadelphia (2002)Google Scholar
  6. 6.
    Shoukry, A.: Collaboration Technologies and Systems (CTS). In: International Conference technologies and Systems, 21–25 May, pp. 546–550 (2012)Google Scholar
  7. 7.
    Alexandra, B., Ralf, S.: Rethinking Sentiment Analysis in the News, Theory to Practice and back‖, European Commission, Joint Research Centre, Department of Software and Computing Systems, University of Alicante, WOMSA, pp. 1–12 (2009)Google Scholar
  8. 8.
    Ding, X., Liu, B., Yu, P.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the International Conference on Web Search and Web Data Mining, pp. 231–240. ACM (2008)Google Scholar
  9. 9.
    Melville, P., Gryc, W., Lawrence, R.: Sentiment analysis of blogs by combining lexical knowledge with text classification. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 1275–1284 (2009)Google Scholar
  10. 10.
    Emma, H., Xiaohui L., Yong S.: The role of text pre-processing in sentiment analysis. Procedia Comput. Sci. Elsevier, 17, 26–32 (2013) [14] Tetlock, P., Saar-Tsechansky, M., Macskassy, S.: More than words: quantifying language to measure firms fundamentals. J. Financ. 63(3), 1437–1467 (2008)Google Scholar
  11. 11.
    Bing, L.: Sentiment Analysis and Opinion Mining, Apr 22 (2012)Google Scholar
  12. 12.
    Melville, P., Gryc, W., Lawrence, R.: Sentiment analysis of blogs by combining lexical knowledge with text classification. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1275–1284. ACM (2009)Google Scholar
  13. 13.
    Jagdale, R.S., Shirsat, V.S., Deshmukh, S.N.: Sentiment analysis of events from twitter using open source tool. Int. J. Comput. Sci. Mob. Comput. 5(4), pp. 475–485 (2016)Google Scholar
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    Bhumika, M., Jadav, V., Vaghela, B.: Sentiment analysis using support vector machine based on feature selection and semantic analysis. Int. J. Comput. Appl. 146(13) (2016)Google Scholar
  16. 16.
    BholaneSavita, D., Deipali, G.: Sentiment analysis on twitter data using support vector machine. Int. J. Comput. Sci. Trends Technol. 4(3) (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Vishal S. Shirsat
    • 1
  • Rajkumar S. Jagdale
    • 1
  • Sachin N. Deshmukh
    • 1
  1. 1.Department of Computer Science and ITB. A. Marathwada UniversityAurangabadIndia

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