• Dr. Sandeep M. Chaware
• Miss. Avanti Pandit
• Miss. Ashwini Pawar
• Miss Pratiksha Tambe
• Miss Nirmal Thakare

Microblogging offerings have revolutionized the way human beings trade facts. Confronted with the ever-growing numbers of tweets with multimedia contents and trending topics, it's far proper to offer visualized summarization to assist users to quickly hold close the essence of topics. While existing works normally attention on text-based strategies best, summarization of a couple of media sorts (e.g., text and image) are scarcely explored. In this paper, proposes a twitter summarization framework to automatically generate visualized summaries for trending topics. Specifically, a novel generative probabilistic model, termed multimodal-LDA (MMLDA), is proposed to find subtopics from microblogs by means of exploring the correlations amongst different media kinds. The contribution work is, to extract microblogs which shows the text, image and video posts. We evaluate the widespread experiments on an actual-international Twitter microblog dataset to illustrate the prevalence of our proposed technique towards the modern-day processes.

Twitter, Tweets, Summarization, Trending Topic, Social Media, OCR, MMLDA

"Proposed Model for Twitter Data Summarization ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.494-501, May-2019, Available :http://www.jetir.org/papers/JETIR1905E73.pdf

"Proposed Model for Twitter Data Summarization ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp494-501, May-2019, Available at : http://www.jetir.org/papers/JETIR1905E73.pdf