Hybrid sentiment classification on twitter aspect-based sentiment analysis

Author:

Zainuddin Nurulhuda,Selamat Ali,Ibrahim Roliana

Funder

Universiti Teknologi Malaysia

Ministry of Higher Education, Malaysia

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference37 articles.

1. Abbasi A (2010) Intelligent feature selection for opinion classification. IEEE Intell Syst 25(4):75–79. https://www.scopus.com

2. Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of twitter data. In: Proceedings of the Workshop on Languages in Social Media, LSM ’11. Association for Computational Linguistics, Stroudsburg, pp 30–38

3. Akhtar MS, Gupta D, Ekbal A, Bhattacharyya P (2017) Feature selection and ensemble construction: A two-step method for aspect based sentiment analysis. Knowl-Based Syst 125:116–135. https://doi.org/10.1016/j.knosys.2017.03.020 . http://www.sciencedirect.com/science/article/pii/S095070511730148X

4. Appel O, Chiclana F, Carter J, Fujita H (2016) A hybrid approach to the sentiment analysis problem at the sentence level. Knowl-Based Syst 108:110–124. https://doi.org/10.1016/j.knosys.2016.05.040 . http://www.sciencedirect.com/science/article/pii/S095070511630137X . New Avenues in Knowledge Bases for Natural Language Processing

5. Baccianella S, Esuli A, Sebastiani F (2010) Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Calzolari N, Choukri K, Maegaard B, Mariani J, Odijk J, Piperidis S, Rosner M, Tapias D (eds) LREC. European Language Resources Association. http://nmis.isti.cnr.it/sebastiani/Publications/LREC10.pdf

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