Aspect Level Sentiment Analysis using Machine Learning Approach: A Comprehensive Review


  • Jyoti Hanvat M.Tech Scholar, Department of CSE, Vaishnavi Institute of Technology and Science, Bhopal (M.P), India
  • Sumit Sharma Professor, Department of CSE, Vaishnavi Institute of Technology and Science, Bhopal (M.P), India



Lexicon-based approach, Sentiment analysis, Natural Language Processing, Machine Learning.


Sentimental analysis is now used from product marketing specific to the detection of social behavior. Progress on Facebook, Twitter, Youtube and other microblogging and social networking sites has not only contributed to a change in social sites, but also to the way we use these sites and the way we do it. People are fundamentally changing their feelings and their points of view with the general public. In this paper a detailed study of different approaches for lexicon-based sentiment analysis are discussed. This paper also shows that efficiency of machine learning over traditional lexicon based sentiment analysis.


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L. Yang, Y. Li, J. Wang and R. S. Sherratt, "Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning," in IEEE Access, Vol. 8, pp. 23522-23530, 2020.

G. Xu, Z. Yu, Z. Chen, X. Qiu and H. Yao, "Sensitive Information Topics-Based Sentiment Analysis Method for Big Data," in IEEE Access, Vol. 7, pp. 96177-96190, 2019.

V. Ramanathan and T. Meyyappan, "Twitter Text Mining for Sentiment Analysis on People’s Feedback about Oman Tourism," 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC), Muscat, Oman, 2019, pp. 1-5.

G. Xu, Y. Meng, X. Qiu, Z. Yu and X. Wu, "Sentiment Analysis of Comment Texts Based on BiLSTM," in IEEE Access, Vol. 7, pp. 51522-51532, 2019.

F. Iqbal et al., "A Hybrid Framework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction," in IEEE Access, Vol. 7, pp. 14637-14652, 2019.

M. Wongkar and A. Angdresey, "Sentiment Analysis Using Naive Bayes Algorithm of The Data Crawler: Twitter," 2019 Fourth International Conference on Informatics and Computing (ICIC), Semarang, Indonesia, 2019, pp. 1-5.

X. Hu, J. Tang, H. Gao, and H. Liu, ‘‘Unsupervised sentiment analysis with emotional signals,’’ in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 607–618.

A. Agarwal, B. Xie, I. Vovsha, O. Rambow, and R. Passonneau, ‘‘Sentiment analysis of Twitter data,’’ in Proc. Workshop Lang. Social Media, 2011, pp. 30–38.

M. Pontiki et al., ‘‘SemEval-2016 task 5: Aspect based sentiment analysis,’’ in Proc. 8th Int. Workshop Semantic Eval. (SemEval), 2014, pp. 27–35.

P. C. S. Njølstad, L. S. Høysæter, W. Wei, and J. A. Gulla, ‘‘Evaluating feature sets and classifiers for sentiment analysis of financial news,’’ in Proc. IEEE/WIC/ACM Int. Joint Conf. Web Intell. (WI) Intell. Agent Technol. (IAT), Vol. 2, Aug. 2014, pp. 71–78

S. Shikhar, et al. “LEXER: LEXicon Based Emotion AnalyzeR.” International Conference on Pattern Recognition and Machine Intelligence. Springer, Cham, 2017.

Low, Lu-Shih Alex, et al. “Content based clinical depression detection in adolescents.” Signal Processing Conference, 2009 17th European. IEEE, 2009.

Wang, Xinyu, et al. “A depression detection model based on sentiment analysis in micro-blog social network.” Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2013.

Wang, Xinyu, Chunhong Zhang, and Li Sun. “An improved model for depression detection in micro-blog social network.” 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). IEEE, 2013.

Shen, Tiancheng, “Cross Domain Depression Detection via Harvesting Social Media.” Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. 2018.




How to Cite

Hanvat, J. ., & Sharma, S. . (2020). Aspect Level Sentiment Analysis using Machine Learning Approach: A Comprehensive Review. SMART MOVES JOURNAL IJOSTHE, 7(4), 21–24.