Comprehensive Survey of Algorithms for Sentiment Analysis

  • V. Seetha LakshmiEmail author
  • B. Subbulakshmi
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 38)


The growth of the web results with a wider increase in online communications. These online communications include reviews, comments and feedbacks that are posted online by the internet users. It is important to discover and analyse their opinion for a better decision making. These opinionated data are analysed using sentiment analysis. Because of its importance in numerous areas, sentiment analysis was adopted as a subject of increasing research interest in the recent years. It is a technique adopted to extract the useful information and identify the user views either as positive or negative. This paper develops a survey on various approaches used in sentiment analysis and a comparative study has also been made along with the elucidation of recent research trends in the sentiment analysis.


Sentiment analysis Opinions Reviews Dictionary Lexicon Corpus 


  1. Wu, C., Wu, F., Wu, S., Yuan, Z., Huang, Y.: A hybrid unsupervised method for aspect term and opinion target extraction. Knowl.-Based Syst. 148, 66–73 (2018)CrossRefGoogle Scholar
  2. Nagarajan, S.M., Gandhi, U.D.: Classifying streaming of Twitter data based on sentiment analysis using hybridization. Neural Comput. Appl. 31, 1425–1433 (2018)CrossRefGoogle Scholar
  3. Alarifi, A., Tolba, A., Al-Makhadmeh, Z., Said, W.: A big data approach to sentiment analysis using greedy feature selection with cat swarm optimization-based long short-term memory neural networks. J. Supercomputing 1–16 (2018)Google Scholar
  4. Zainuddin, N., Selamat, A., Ibrahim, R.: Hybrid sentiment classification on Twitter aspect-based sentiment analysis. Appl. Intell. 48, 1218–1232 (2018)Google Scholar
  5. Dridi, A., Atzeni, M., Recupero, D.R.: FineNews: fine-grained semantic sentiment analysis on financial microblogs and news. Int. J. Mach. Learn. Cybern. 1–9 (2018)Google Scholar
  6. Deshmukh, J.S., Tripathy, A.K.: Entropy based classifier for cross-domain opinion mining. Appl. Comput. Inform. 14(1), 55–64 (2018)CrossRefGoogle Scholar
  7. Han, H., Zhang, J., Yang, J., Shen, Y., Zhang, Y.: Generate domain-specific sentiment lexicon for review sentiment analysis. Multimedia Tools Appl. 77(16), 21265–21280 (2018)CrossRefGoogle Scholar
  8. Teso, E., Olmedilla, M., Martínez-Torres, M.R., Toral, S.L.: Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective. Technol. Forecasting Soc. Change 129, 131–142 (2018)CrossRefGoogle Scholar
  9. Yang, H.C., Lee, C.H., Wu, C.Y.: Sentiment discovery of social messages using self-organizing maps. Cogn. Comput. 10(6), 1152–1166 (2018)CrossRefGoogle Scholar
  10. Ali, F., Kwak, D., Khan, P., Islam, S.R., Kim, K.H., Kwak, K.S.: Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling. Transp. Res. Part C: Emerg. Technol. 77, 33–48 (2017)CrossRefGoogle Scholar
  11. Schouten, K., Van Der Weijde, O., Frasincar, F., Dekker, R.: Supervised and unsupervised aspect category detection for sentiment analysis with co-occurrence data. IEEE Trans. Cybern. 48(4), 1263–1275 (2017)CrossRefGoogle Scholar
  12. Zhao, W., Guan, Z., Chen, L., He, X., Cai, D., Wang, B., Wang, Q.: Weakly-supervised deep embedding for product review sentiment analysis. IEEE Trans. Knowl. Data Eng. 30(1), 185–197 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of CSEThiagarajar College of EngineeringMaduraiIndia

Personalised recommendations