Published January 7, 2020 | Version v1
Journal article Open

Text Data Analysis for Advertisement Recommendation System Using Multi-label Classification of Machine Learning

  • 1. PG Student, Department of Computer Science and Engineering, Walchand Institute of Technology, P.A.H. Solapur University, Solapur, Maharashtra, India
  • 2. Professor, Department of Computer Science and Engineering, Walchand Institute of Technology, P.A.H. Solapur University, Solapur, Maharashtra, India

Description

Everyone today can access the streaming content on their mobile phones, laptops very easily and video has been a very important and popular content on the internet. Nowadays, people are making their content and uploading it on the streaming platforms so the size of the video dataset became massive compared to text, audio and image datasets. So, providing advertisements on the video related to the topic of video will help to boost business. In this proposed system the title and description of video will be taken as input to classify the video using a natural language processing text classification method. Aim of Natural Language Processing is to solve the text classification problem by analyzing the contents of text data and decide its category. The proposed system would extract features from videos like title, description, and hashtags based on these extracted features we intend producing classification labels with the use of multi-label classification models. Analyzing produced labels concerning advertisement datasets we intend to provide advertisements on the video related to the topic of the video.  

Files

(1-6)Text Data Analysis.pdf

Files (186.5 kB)

Name Size Download all
md5:5d17dfece27e98f43a791dd964631a72
186.5 kB Preview Download

Additional details

References

  • Ayon Dey (2016), "Machine learning algorithms - A Review", International Journal of Computer Science and Information Technologies, Volume 7, Issue 3, pp. 1174-1179
  • Eva Gibaja, Sabastian Ventura. (November 2014) "Multi-label learning: A review of the state of the art and ongoing research", Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery., pp.1-46
  • Gangadhara Rao Kommu, M.Trupthi, Suresh Pabboju (1-2 August 2014), "A novel approach for multi-label classification using probabilistic classifiers", IEEE International Conference on Advances in Engineering & Technology Research (ICAETR), Unnao, India
  • Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang Wei Xu1, (2016), "CNN-RNN: A unified framework for multi-label image classification", Computer Vision and Pattern Recognition, pp. 1-10
  • Kwangsoo Shin, Junhyeong Jeon, Seungbin Lee, Boyoung Lim, Minsoo Jeong, Jongho Nang (2016), "Approach for video classification with multi-label on YouTube-8M dataset", IEEE Conference on Computer Vision and Pattern Recognition, pp. 5297-5307

Subjects