• SANA
• CH. B. N. LAKSHMI

Social media platforms, for instance as twitter has become increasingly popular as an emerging platform for messaging and communication. On the other side, online meticulous dealing with multidimensional sorted out data. The purpose of using Hierarchical topic modeling i.e., THLDA to mine the dimension hierarchy of tweets. It can be applied for text OLAP on the tweets. And it uses word2vec to analyze the semantic relationships of words in tweets to obtain more effective dimensions. To improve the model effectiveness the bicliques to calculate the semantic impact of the topic of two tweets. Focusing on how the social impact factors and word semantic similarity influence the experimental results separately. In this we are considering how social relationships impact on the hierarchical topic model. In social relationships we are focusing direct and indirect relationships to follow the unrelated tweeters. We conduct extensive experiments on real twitter data to evaluate to effectiveness of THLDA. We are using hash tags to improve the models.

Social Media Relationship, Bicliques, Semantic Relationship, Online Analytical Processing, Hierarchical Latent Dirichlet Allocation(THLDA)

"ANALYSIS OF SOCIAL RELATIONSHIPS ON TWITTER DATA FOR ONLINE ANALYTICAL PROCESSING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 10, page no.85-91, October-2020, Available :http://www.jetir.org/papers/JETIR2010008.pdf

"ANALYSIS OF SOCIAL RELATIONSHIPS ON TWITTER DATA FOR ONLINE ANALYTICAL PROCESSING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 10, page no. pp85-91, October-2020, Available at : http://www.jetir.org/papers/JETIR2010008.pdf