Prediction of Users Behavior on the Social Media Using XGBRegressor
- Title
- Prediction of Users Behavior on the Social Media Using XGBRegressor
- Creator
- Tahseen S.; Danti A.
- Description
- The previous decennium has seen the growth and advance with respect to social media and such that has violently also immensely expanded to infiltrate each side of user lives. In addition, mobile network empowers clients to admittance to MSNs at whenever, anyplace, for any character, including job and gathering. Accordingly, the association practices among clients and MSNs are getting completer and more confounded. The goal of this paper is to examine the number of followers, likes, and post for Instagram users. The dataset yielded several fundamental features, which were used to create the model with multimedia social networks (MSNs). Then, natural language processing (NLP) features should be added and finally incorporate features derived in distinction to a machine learning technique like XGBRegressor with TF-IDF technique. We use two performance indicators to compare the different models: root mean square error (RMSE) and the R2 value. We achieved average accuracy using XGBRegressor which is 82%. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Source
- Lecture Notes on Data Engineering and Communications Technologies, Vol-111, pp. 491-502.
- Date
- 2022-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- NLP; Social media; XGBRegressor
- Coverage
- Tahseen S., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India; Danti A., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 23674512
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Tahseen S.; Danti A., “Prediction of Users Behavior on the Social Media Using XGBRegressor,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/18665.