Skip to main content
SpringerLink
Log in

Bacterial foraging information swarm optimizer for detecting affective and informative content in medical blogs

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The web has turned out to be an ever-present source of knowledge as well as information with which individuals can contribute to show their performance. Typically, content on the web may be sorted into two categories: the first being a user’s personal sentiments or opinions which are known as affective content, and the second being factual information regarding events or technology which is understood as informative content. In the current work, an hybrid multi stage optimization named ‘Bacterial Foraging Information Swarm Optimizer’ as a novel algorithm is proposed to classify the affective content and the informative content from the medical weblogs. In order to enhance this algorithm and to evaluate its accuracy, the medical data source such as MAYO clinic data is taken for the consideration of classification of information as well as affective content. The expansion of the web permits consumers to present their views and opinions online by way of blogs, videos or social networking sites that offer data with regard to particular products or services. Applications are numerous in new generation organizations, products for managing reputations, perceptions of online markers or even monitoring of online content. The current work examines this multistage optimization algorithm for selecting the relevant information at first stage which is followed by the optimization in the second stage and finally as post processing task the classification protocols is applied to contrasts their performance. The valuation is carried out by using opinions gathered from reviews from medical blogs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Ni, X., Xue, G.R., Ling, X., Yu, Y., Yang, Q.: Exploring in the weblog space by detecting informative and affective articles. In: Proceedings of the 16th International Conference on World Wide Web, pp. 281–290. ACM (2007)

  2. Denecke, K.: Accessing medical experiences and information. In: European Conference on Artificial Intelligence, Workshop on Mining Social Data 21, 1–15 (2008)

  3. Onan, A., Korukoglu, S.: Ensemble methods for opinion mining. In: 23rd Signal Processing and Communications Applications Conference (SIU 2015), IEEE, pp. 212–215 (2015)

  4. Jiang, P., Zhang, C., Fu, H., Niu, Z., Yang, Q.: An approach based on tree kernels for opinion mining of online product reviews. In: 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, pp. 256–265 (2010)

  5. Subban, R., Susitha, N., Mankame, D.P.: Efficient iris recognition using Haralick features based extraction and fuzzy particle swarm optimization. Clust. Comput. 1(1), 1–12 (2017)

  6. Ali, Z., Shahzad, W.: EPACO: a novel ant colony optimization for emerging patterns based classification. Clust. Comput. 1(1), 1–15 (2017)

    Google Scholar 

  7. Wu, Q., Liu, H., Yan, X.: Multi-label classification algorithm research based on swarm intelligence. Clust. Comput. 19(4), 2075–2085 (2016)

  8. Bermejo, P., Gámez, J.A., Puerta, J.M.: Speeding up incremental wrapper feature subset selection with Naive Bayes classifier. Knowl. Based Syst. 55, 140–147 (2014)

    Article  Google Scholar 

  9. Mezura-Montes, E., Hernández-Ocana, B.: Bacterial foraging for engineering design problems: preliminary results. In: Memorias del 4o Congreso Nacional de Computacion Evolutiva (COMCEV’2008), CIMAT, Gto., Mexico (2008)

  10. Niu, B., Wang, H., Wang, J., Tan, L.: Multi-objective bacterial foraging optimization. Neurocomputing 116, 336–345 (2013)

    Article  Google Scholar 

  11. Yevseyeva, I., Guerreiro, A.P., Emmerich, M.T., Fonseca, C.M.: A portfolio optimization approach to selection in multiobjective evolutionary algorithms. In: International Conference on Parallel Problem Solving from Nature, pp. 672–681 (2014)

  12. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)

    Article  Google Scholar 

  13. Alsaffar, A., Omar, N.: Integrating a Lexicon based approach and K nearest neighbour for Malay sentiment analysis. J. Comput. Sci. 11(4), 639–645 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Computing, Veltech Dr. RR & Dr. SR University, Avadi, Chennai, Tamil Nadu, 600 062, India

    E. A. Neeba & S. Koteeswaran

Authors
  1. E. A. Neeba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Koteeswaran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. A. Neeba.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Neeba, E.A., Koteeswaran, S. Bacterial foraging information swarm optimizer for detecting affective and informative content in medical blogs. Cluster Comput 22 (Suppl 5), 10743–10756 (2019). https://doi.org/10.1007/s10586-017-1169-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1169-9

Keywords

Search

Navigation