Abstract
An increasing number of people are changing their way of thinking by reading news headlines. The interactivity and sincerity present in online news headlines are becoming influential to society. Apart from that, news websites build efficient policies to catch people’s awareness and attract their clicks. In that case, it is a must to identify the sentiment polarity of the news headlines for avoiding misconception. In this paper, we analyze 3383 news headlines generated by five major global newspapers during a minimum of four consecutive months. In order to identify the sentiment polarity (or sentiment orientation) of news headlines, we use 7 machine learning algorithms and compare those results to find the better ones. Among those Bernoulli Naïve Bayes technique achieves higher accuracy than others. This study will help the public to make any decision based on news headlines by avoiding misconception against any leader or governance and will help to identify the most neutral newspaper or news blogs.
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References
Ecker, U.K., Lewandowsky, S., Chang, E.P., Pillai, R.: The effects of subtle misinformation in news headlines. J. Exp. Psychol. Appl. 20(4), 323 (2014)
Nardo, M., Petracco-Giudici, M., Naltsidis, M.: Walking down wall street with a tablet: a survey of stock market predictions using the web. J. Econ. Surv. 30(2), 356–369 (2016)
Alanyali, M., Moat, H.S., Preis, T.: Quantifying the relationship between financial news and the stock market. Sci. Rep. 3, 3578 (2013)
Feuerriegel, S., Heitzmann, S.F., Neumann, D.: Do investors read too much into news? How news sentiment causes price formation. In: 2015 48th Hawaii International Conference on System Sciences, pp. 4803–4812. IEEE (2015)
Rish, I., et al.: An empirical study of the Naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, pp. 41–46 (2001)
Rennie, J.D., Shih, L., Teevan, J., Karger, D.R.: Tackling the poor assumptions of Naive Bayes text classifiers. In: Proceedings of the 20th International Conference on Machine Learning, ICML 2003, pp. 616–623 (2003)
McCallum, A., Nigam, K., et al.: A comparison of event models for Naive Bayes text classification. In AAAI-98 Workshop on Learning for Text Categorization, vol. 752, pp. 41–48. Citeseer (1998)
Ho, T.K., Hull, J.J., Srihari, S.N.: Decision combination in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 1, 66–75 (1994)
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) COMPSTAT 2010. Physica-Verlag HD (2010)
Gunn, S.R., et al.: Support vector machines for classification and regression. ISIS technical report, vol. 14, no. 1, pp. 5–16 (1998)
Schölkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Comput. 12(5), 1207–1245 (2000)
Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)
Chiong, R., Fan, Z., Hu, Z., Adam, M.T., Lutz, B., Neumann, D.: A sentiment analysis-based machine learning approach for financial market prediction via news disclosures. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 278–279. ACM (2018)
El-Din Mohamed Hussein, D.M.: A survey on sentiment analysis challenges. J. King Saud. Univ. Eng. Sci. 30(4), 330–338 (2018)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)
Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th International Conference on World Wide Web, pp. 342–351. ACM (2005)
Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data mining, pp. 231–240. ACM (2008)
Im, T.L., San, P.W., On, C.K., Alfred, R., Anthony, P.: Analysing market sentiment in financial news using lexical approach. In: 2013 IEEE Conference on Open Systems, ICOS, pp. 145–149. IEEE (2013)
Godbole, N., Srinivasaiah, M., Skiena, S.: Large-scale sentiment analysis for news and blogs. In: ICWSM 2007, vol. 21, pp. 219–222 (2007)
Maynard, D., Bontcheva, K., Rout, D.: Challenges in developing opinion mining tools for social media. In: Proceedings of the@ NLP can u tag# usergeneratedcontent, pp. 15–22 (2012)
Lerman, K., Gilder, A., Dredze, M., Pereira, F.: Reading the markets: forecasting public opinion of political candidates by news analysis. In: Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1, pp. 473–480. Association for Computational Linguistics (2008)
Kim, S., Hovy, E.: Crystal: analyzing predictive opinions on the web. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (2007)
Shuhidan, S.M., Hamidi, S.R., Kazemian, S., Shuhidan, S.M., Ismail, M.A.: Sentiment analysis for financial news headlines using machine learning algorithm. Proceedings of the 7th International Conference on Kansei Engineering and Emotion Research 2018. AISC, vol. 739, pp. 64–72. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8612-0_8
Severyn, A., Moschitti, A.: Twitter sentiment analysis with deep convolutional neural networks. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 959–962. ACM (2015)
Araque, O., Corcuera-Platas, I., Sanchez-Rada, J.F., Iglesias, C.A.: Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Syst. Appl. 77, 236–246 (2017)
Tang, D., Qin, B., Liu, T.: Deep learning for sentiment analysis: successful approaches and future challenges. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 5(6), 292–303 (2015)
Taylor, A., Marcus, M., Santorini, B.: The Penn Treebank: an overview. In: Abeillé, A. (ed.) Treebanks. TLTB, vol. 20, pp. 5–22. Springer, Dordrecht (2003). https://doi.org/10.1007/978-94-010-0201-1_1
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Rahman, S., Hossain, S.S., Islam, S., Chowdhury, M.I., Rafiq, F.B., Badruzzaman, K.B.M. (2019). Context-Based News Headlines Analysis Using Machine Learning Approach. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11684. Springer, Cham. https://doi.org/10.1007/978-3-030-28374-2_15
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