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%.
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References
Roebuck, K.: Sentiment Analysis: High-Impact Strategies What You Need to Now: Definitions, Adoptions, Impact, Benefits, Maturity. Vendors, Emereo Publishing, 05 Nov 2012
Pooja, P., Sharvari, G.: A survey of sentiment classification techniques used for indian regional languages. Int. J. Comput. Sci. Appl. 5(2) April 2015
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)
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)
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)
Shoukry, A.: Collaboration Technologies and Systems (CTS). In: International Conference technologies and Systems, 21–25 May, pp. 546–550 (2012)
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)
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)
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)
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)
Bing, L.: Sentiment Analysis and Opinion Mining, Apr 22 (2012)
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)
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)
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)
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)
BholaneSavita, D., Deipali, G.: Sentiment analysis on twitter data using support vector machine. Int. J. Comput. Sci. Trends Technol. 4(3) (2016)
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Shirsat, V.S., Jagdale, R.S., Deshmukh, S.N. (2019). Sentence Level Sentiment Identification and Calculation from News Articles Using Machine Learning Techniques. In: Iyer, B., Nalbalwar, S., Pathak, N. (eds) Computing, Communication and Signal Processing . Advances in Intelligent Systems and Computing, vol 810. Springer, Singapore. https://doi.org/10.1007/978-981-13-1513-8_39
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DOI: https://doi.org/10.1007/978-981-13-1513-8_39
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