Advertisement

An ANN-Based Text Mining Approach Over Hash Tag and Blogging Text Data

  • Archana TamrakarEmail author
  • Pradeep Mewada
  • Purva Gubrele
  • Ritu PrasadEmail author
  • Praneet SaurabhEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1057)

Abstract

In the daily life, everybody keeps on using Internet after the waking up that includes the communication among different people of this world. On one side, Internet has made everyone’s life very convenient and provides many facilities that can be used on social networking sites such as chat, messaging, comment, and blogging. This way everyone keeps on sharing the personal data at different places like Web sites, chats, social media. The text mining can be defined as the process of finding useful information from the given text. There exist various methods that remain helpful in analyzing texts and extracting the information but these often suffer from various complexities. Also, theoretically, it is quite difficult to analyze and extract the information from these raw data. This paper presents an Effective feed forward artificial neural network (FP-ANN) for text mining that generates different textual patterns from several resources and provides results very precisely with lesser computation time, lower cost, and overhead. FP-ANN approach calculates the hash label and discovers importance between inputs for text mining.

Keywords

Classification Information retrieval Text mining Artificial neural network 

References

  1. 1.
    Fong, S., Gao, E., Wong, R.: Optimized swarm search-based feature selection for text mining in sentiment analysis. In: IEEE 15th International Conference on Data Mining Workshop, pp. 1153–1162 (2015)Google Scholar
  2. 2.
    Fong, S., Deb, S., Yang, X.S., Li, J.: Feature selection in life science classification, metaheuristic swarm search. IEEE IT Prof. 16(4), 24–29 (2014)CrossRefGoogle Scholar
  3. 3.
    Sagayam, R.: A survey of text mining: retrieval, extraction and indexing techniques. Int. J. Comput. Eng. Res. 2(5), 1443–1446 (2012)Google Scholar
  4. 4.
    Padhy, N., Mishra, D., Panigrahi, R.: The survey of data mining applications and feature scope. Int. J. Comput. Sci. Eng. Inf. Technol. (IJCSEIT) 2(3), 43–58 (2012)Google Scholar
  5. 5.
    Fan, W., Wallace, L., Rich, S., Zhang, Z.: Tapping the power of text mining. Commun. ACM 49(9), 76–82 (2006)CrossRefGoogle Scholar
  6. 6.
    Weiss, S.M., Indurkhya, N.T., Zhang, T., Damerau, F.: Text Mining: Predictive Methods for Analyzing Unstructured Information, pp. 157–195. Springer (2010)Google Scholar
  7. 7.
    Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems, pp. 325–341. Springer (2007)Google Scholar
  8. 8.
    Ferrer, J., Kruse, P.M., Chicano, F.E., Alba, E.: Search based algorithms for test sequence generation in functional testing. Inf. Softw. Technol. 58, 419–432 (2015)CrossRefGoogle Scholar
  9. 9.
    Ferragina, P., Piccinno, F., Santoro, R.: On analyzing hashtags in twitter. In: Proceedings of the Ninth International AAAI Conference on Web and Social Media, pp. 110–119 (2015)Google Scholar
  10. 10.
    Bart, P., Knijnenburg, M.C., Willemsen, K.A.: A pragmatic procedure to support the user-centric evaluation of recommender systems. In: RecSys’11, pp. 321–324. ACM (2011)Google Scholar
  11. 11.
    Bahdanau, D., Cho, K., Bengio Y.: Neural machine translation by jointly learning to align and translate. In: International Conference on Learning Representations, pp. 1–15 (2015)Google Scholar
  12. 12.
    Bandyopadhyay, A., Ghosh, K., Majumder, P., Mitra, M.: Query expansion for microblog retrieval. Int. J. Web Sci. 1(4), 368–380 (2012)CrossRefGoogle Scholar
  13. 13.
    Teppan, E.C.: Implications of psychological phenomenons for recommender systems. In: RecSys’08, pp. 323–326. ACM (2008)Google Scholar
  14. 14.
    Saurabh, P., Verma, B.: An efficient proactive artificial immune system based anomaly detection and prevention system. Expert Syst. Appl. 60, 311–320 (2016). ElsevierCrossRefGoogle Scholar
  15. 15.
    Saurabh, P., Verma, B,: Immunity inspired cooperative agent based security system. Int. Arab. J. Inf. Technol. 15(2), 289–295 (2018)Google Scholar
  16. 16.
    Saurabh, P., Verma, B., Sharma, S.: An immunity inspired anomaly detection system: a general framework a general framework. In: 7th International conference on bio-inspired computing: theories and applications (BIC-TA 2012), vol 202, AISC, pp. 417–428. Springer (2012)Google Scholar
  17. 17.
    Saurabh, P., Verma, B, Sharma, S.: Biologically Inspired Computer Security System: The Way Ahead, Recent Trends in Computer Networks and Distributed Systems Security, CCIS, vol. 335, pp. 474-484. Springer (2011)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Technocrats Institute of Technology (Advance)BhopalIndia
  2. 2.Technocrats Institute of TechnologyBhopalIndia

Personalised recommendations