Abstract
This era of computing made everything computerized. So people started to interact in the virtual life more than real life. From shopping to talking, everything is controlled by computer. Because of that it has become more and more important to analyze and extract sentiment. Sentiment can be extracted from different type of data format like audio, text, image. In this paper, we proposed some methods to analyze text data in the paragraph level. Those methods can be implemented using bag of words, and priority was given to the lexical-based analysis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Krishna, D.S., Kulkarni, G.A., Mohan, A.: Sentiment analysis-time variant analytics. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5(3) (2015). ISSN. 2277 128X
Celikyilmaz, A., Hakkani-Tur, D., Feng J.: Probabilistic model based sentiment analysis of twitter messages. In: Spoken Language Technology Workshop (SLT), 2010, pp. 79–84. IEEE (2010)
Ferguson, P., O’Hare, N., Davy, M., Bermingham, A., Tattersall, S., Sheridan, P., Gurrin, C., Smeaton, A.F.: Exploring the use of paragraph-level annotations for sentiment analysis of financial blogs. In: WOMAS 2009—Workshop on Opinion Mining and Sentiment Analysis, Seville, Spain (2009)
Pamungkas, E.W., Putri, D.G.P.: Word sense disambiguation for lexicon-based sentiment analysis. In: 9th International Conference on Machine Learning and Computing, Singapore, Singapore, pp. 442–446 (2017)
Laura, C., José, O., Mathieu R., Pascal, P.: Dictionary-based sentiment analysis applied to a specific domain. In: Information Management and Big Data, pp. 57–68, Springer International Publishing (2017)
Ekawati, D., Khodra, M.L.: Aspect-based sentiment analysis for Indonesian restaurant reviews. In: International Conference on Advanced Informatics, Concepts, Theory, and Applications, Denpasar, Indonesia (2017)
Trilla, A., AlÃas, F.: Sentence-based sentiment analysis for expressive text-to-speech. IEEE Trans. Audio Speech Lang. Process. 21(2), 223–233 (2013)
Sanguansat, P.: Paragraph2Vec-based sentiment analysis on social media for business in Thailand. In: 8th International Conference on Knowledge and Smart Technology, Chiangmai, Thailand (2016)
Nguyen, H., Nguyen M.L.: A deep neural architecture for sentence-level sentiment classification in twitter social networking. In: Conference of the Pacific Association for Computational Linguistics, abs/1706.08032 (2017)
Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment strength detection for the social web. J. Am. Soc. Inf. Sci. Technol. 63(1), 163–173 (2012)
Dutta, S., Roy, M., Das, A.K., Ghosh, S.: Sentiment detection in online content: a WordNet based approach. In: Panigrahi, B., Suganthan, P., Das, S. (eds.) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science, vol. 8947. Springer, Cham (2015)
Acknowledgements
We would like to thank DIU-NLP and Machine Learning Research LAB for all their support and help. Any error in this research paper is our own and should not tarnish the reputations of these esteemed persons.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chowdhury, S.M.M.H., Abujar, S., Saifuzzaman, M., Ghosh, P., Hossain, S.A. (2019). Sentiment Prediction Based on Lexical Analysis Using Deep Learning. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 814. Springer, Singapore. https://doi.org/10.1007/978-981-13-1501-5_38
Download citation
DOI: https://doi.org/10.1007/978-981-13-1501-5_38
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1500-8
Online ISBN: 978-981-13-1501-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)