Abstract
Aim of this article is to propose a text preprocessing model for sentiment analysis (SA) over twitter posts with the help of Natural Language processing (NLP) techniques. Discussions and investments on health-related chatter in social media keep on increasing day by day. Capturing the actual intention of the tweeps (twitter users) is challenging. Twitter posts consist of Text. It needs to be cleaned before analyzing and we should reduce the dimensionality problem and execution time. Text preprocessing plays an important role in analyzing health-related tweets. We gained 5.4% more accurate results after performing text preprocessing and overall accuracy of 84.85% after classifying the tweets using LASSO approach.
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References
Fox, S.: The social life of health information. Pew Internet Am. Life Proj, pp. 1–33 (2011)
Shearer, E., Gottfried, J.: News Use Across Social Media Platforms (2017)
Baldwin, T., Cook, P., Lui, M., Mackinlay, A., Wang, L.: How noisy social media text, how diffrnt social media sources? Proc. IJCNLP, 356–364 (2013)
Sarker, A., Gonzalez, G.: Data, tools and resources for mining social media drug chatter. Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining, pp. 99–107 (2016)
Angiani, G., Ferrari, L., Fontanini, T., Fornacciari, P., Iotti, E., Magliani, F., Manicardi, S.: A comparison between preprocessing techniques for sentiment analysis in Twitter. In: CEUR Workshop Proceeding 1748 (2016)
Nirmal, V.J., Amalarethinam, D.I.G.: Parallel implementation of big data pre-processing algorithms for sentiment analysis of social networking data. Int. J. Fuzzy Math. Arch. 6, 149–159 (2015)
Bao, Y., Quan, C., Wang, L., Ren, F.: The role of pre-processing in twitter sentiment analysis. Lecture Notes in Computuer Science (including Subser. Lecture Notes in Artificial Intelligence, Lecture Notes in Bioinformatics) 8589 LNAI, pp. 615–624 (2014)
Prrllochs, N., Feuerriegel, S., Neumann, D.: Generating domain-specific dictionaries using bayesian learning. SSRN Electron. J. (2014)
Maurya, A.K., Unit, A.S.: Data Sharing and Resampled LASSO : A Word Based Sentiment Analysis for IMDb Data, pp. 1–19 (2017)
Dos Santos, F.L., Ladeira, M.: The role of text pre-processing in opinion mining on a social media language dataset. In: Proceedings—2014 Brazilian Conference on Intelligent System, BRACIS 2014, pp. 50–54 (2014)
Hemalatha, I., Varma, D.G.P.S., A. Govardhan, D.: Preprocessing the informal data for efficient sentiment analysis. Int. J. Emerg. Trends Technol. Comput. Sci. 1, 58 (2012)
Haddi, E., Liu, X., Shi, Y.: The role of text pre-processing in sentiment analysis. Procedia Comput. Sci. 17, 26–32 (2013)
Jianqiang, Z., Xiaolin, G.: Comparison research on text pre-processing methods on twitter sentiment analysis. IEEE Access. 5, 2870–2879 (2017)
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Pavan Kumar, C.S., Dhinesh Babu, L.D. (2019). Novel Text Preprocessing Framework for Sentiment Analysis. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 105. Springer, Singapore. https://doi.org/10.1007/978-981-13-1927-3_33
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DOI: https://doi.org/10.1007/978-981-13-1927-3_33
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