Submit your paper : editorIJETjournal@gmail.com

Paper Title : Latent Dirichilet Allocation And Naive Bayes Clasification Based Twitter Data’S Hierarchical Topic Modeling

ISSN : 2395-1303
Year of Publication : 2020
10.29126/23951303/IJET-V6I2P1

Authors: Mr. C. Mani, M C A, M.E, M Phil, Mr. P.Harish, M C A

         


Abstract
Twitter data accumulated so far make it in all likelihood to find out the distribution and go with the flow of mass tastes and opinions, which greatly help in product recommendation, target advertising and marketing and so on. This challenge proposes a subject model known as twitter hierarchical Latent Dirichlet Allocation (thLDA). Based on hierarchical latent Dirichlet allocation, thLDA aims to automatically mine the hierarchical measurement of tweets’ subjects, which may be further employed for text Here, the phrases present in every of the topics with extra importance are extracted out and their beta value is found out. Furthermore, thLDA analyzes the relationships of words in tweets to get a more powerful measurement. Extensive experiments are carried out on Twitter information and the effectiveness of thLDA is evaluated. The consequences show that it outperforms properly amongst other current topic fashions in mining. This venture improves the topics mining amongst two subjects as well as three subjects. In addition, conditional chance is finished for Naïve Bayes Classification of important terms in the given statistics set so that the whole words possibilities in all the categories are found out and displayed. The task is designed the usage of R Studio 1.0. The coding language used is R 3.4.4.

Reference
[1] D. Yu, J. Sun, Y. Wu, Z. Ni, and Y. Li, ‘‘Discovering hidden interests from Twitter for multidimensional analysis,’’ in Proc. 29th Int. Conf. Softw. Eng. Knowl. Eng., 2017, pp. 329–334. [2] M. Azabou, K. Khrouf, J. Feki, C. Soulé-Dupuy, and N. Vallès, ‘‘A novel multidimensional model for the OLAP on documents: Modeling, generation and implementation,’’ in Proc. Int. Conf. Model Data Eng. Cham, Switzerland: Springer, 2014, pp. 258–272. [3] Q. Li, S. Shah, X. Liu, A. Nourbakhsh, and R. Fang, ‘‘Tweet topic classification using distributed language representations,’’ in Proc. IEEE/WIC/ACM Int. Conf. Web In tell. (WI), Oct. 2016, pp. 81–88. [4] X. Liu et al., ‘‘A text cube approach to human, social and cultural behavior in the Twitter stream,’’ in Proc. Int. Conf. Social Comput., Behav.-Cultural Modeling, Predict. Berlin, Germany: Springer, 2013, pp. 321–330. [5] N. U. Rehman, A. Weiler, and M. H. Scholl, ‘‘OLAPing social media: The case of Twitter,’’ in Proc. IEEE/ACM Int. Conf. Adv. Social Netw. Anal. Mining, Aug. 2013, pp. 1139–1146. [6] X. Pu, M. A. Chatti, H. Thues, and U. Schroeder, ‘‘Wiki-LDA: A mixed method approach for effective interest mining on Twitter data,’’ in Proc. 8th Int. Conf. Comput. Supported Edu. Rome, Italy: ScitePressScience and Technology Publications, 2016, pp. 426– 433. [7] A. M. Dai and A. J. Storkey, ‘‘the supervised hierarchical Dirichlet process,’’ IEEE Trans. Pattern Anal. Mach. In tell vol. 37, no. 2, pp. 243–255, Feb. 2015. [8] J.-T. Chien, ‘‘Hierarchical Pitman–Yor–Dirichlet language model,’’ IEEE/ACM Trans. Audio, Speech, Language Process, vol. 23, no. 8, pp. 1259–1272, Aug. 2015. [9] D. Ganguly, D. Roy, M. Mitra, and G. J. F. Jones, ‘‘Word embedding based generalized language model for information retrieval,’’ in Proc. 38th Int. ACM SIGIR Conf. Res. Develop. Inf. Retr., 2015, pp. 795–798. [10] Haixia Liu, “Sentiment Analysis of Citations Using Word2vec”, Computation and Language (cs.CL), arXiv: 1704.00177v1 [cs.CL], April 2017

Keywords
LDA, Navive Bayes, OLAP, Data Mining