Abstract: A method for predictive trend analytics with social media information is proposed for marketing. Through keyword analysis, page view analysis, access analysis, heat map analysis, Google Analytics, real time analysis, company and competitor analysis, trend analysis with the social media data derived from X (former tweeter), Instagram, Facebook, YouTube, TikTok, market trend can be predicted. The proposed method is created in a local server and is extended to AWS cloud. The proposed system, also ensure negative / positive analysis from the acquired social media information. Through some experiments, it is found that by using AI to analyze social data by category, you can visualize the degree of attention for each keyword, model relationships between information, identify trending keywords, and where the keywords are in their lifecycle. It turns out that it's possible to categorize which ones exist and predict which ones will scale up in the next six months. In addition, corporate product development and marketing personnel can identify themes, materials, benefits, etc. that have signs of becoming popular based on insights based on predictive behavioral data obtained from the proposed method and system and utilize them in new business development and new product planning.

Kohei Arai, Ikuya Fujikawa, Yusuke Nakagawa and Sayuri Ogawa, “Method for Predictive Trend Analytics with SNS Information for Marketing” International Journal of Advanced Computer Science and Applications(IJACSA), 15(2), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150245

@article{Arai2024,
title = {Method for Predictive Trend Analytics with SNS Information for Marketing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150245},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150245},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {2},
author = {Kohei Arai and Ikuya Fujikawa and Yusuke Nakagawa and Sayuri Ogawa}
}