Exploring a Supervised Learning Based Social Media Business Sentiment Index

2017 ◽  
pp. 193-202
Author(s):  
Hyeonseo Lee ◽  
Harim Seo ◽  
Nakyeong Lee ◽  
Min Song
Keyword(s):  
Social Media ◽  
Supervised Learning

scholarly journals Developing a supervised learning-based social media business sentiment index

2019 ◽  
Vol 76 (5) ◽  
pp. 3882-3897 ◽  
Author(s):  
Hyeonseo Lee ◽  
Nakyeong Lee ◽  
Harim Seo ◽  
Min Song
Keyword(s):  
Social Media ◽  
Supervised Learning

Constrained NMF-based semi-supervised learning for social media spammer detection

2017 ◽  
Vol 125 ◽  
pp. 64-73 ◽  
Author(s):  
Dingguo Yu ◽  
Nan Chen ◽  
Frank Jiang ◽  
Bin Fu ◽  
Aihong Qin
Keyword(s):  
Social Media ◽  
Supervised Learning

scholarly journals Semi-supervised learning for sentiment analysis in mass social media

2014 ◽  
Vol 24 (5) ◽  
pp. 482-488 ◽  
Author(s):  
Sola Hong ◽  
Yeounoh Chung ◽  
Jee-Hyong Lee
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Supervised Learning

Toward a Social Media Emergency Events Detection Based on Supervised Learning

2021 ◽  
pp. 265-274
Author(s):  
Yahir Mendoza ◽  
Jorge Santillan ◽  
Roberth Alcivar-Cevallos ◽  
Jorge Parraga-Alava
Keyword(s):  
Social Media ◽  
Supervised Learning ◽  
Emergency Events ◽  
Events Detection

Follower Link Prediction using the XGBoostClassification Model with Multiple Graph Features

2021 ◽  
Author(s):  
Dayal Kumar Behera ◽  
Madhabananda Dash ◽  
Subhra Swetanisha ◽  
Janmenjoy Nayak ◽  
S Vimal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Supervised Learning ◽  
Link Prediction ◽  
Prediction Models ◽  
Binary Classification ◽  
Prediction Problem ◽  
Global Features ◽  
Proposed Model ◽  
Real World Datasets

Abstract The Follower Link Prediction is an emerging application preferred by social networking sites to increase their user network. It helps in finding potential unseen individual and can be used for identifying relationship between nodes in social network. With the rapid growth of many users in social media, which users to follow leads to information overload problems. Previous works on link prediction problem are generally based on local and global features of a graph and limited to a smaller dataset. The number of users in social media is increasing in an extraordinary rate. Generating features for supervised learning from a large user network is challenging. In this paper, a supervised learning model (LPXGB) using XGBoost is proposed to consider the link prediction problem as a binary classification problem. Many hybrid graph feature techniques are used to represent the dataset suitable for machine learning. The efficiency of the LPXGB model is tested with three real world datasets Karate, Polblogs and Facebook. The proposed model is compared with various machine learning classifiers and also with traditional link prediction models. Experimental results are evident that the proposed model achieves higher classification accuracy and AUC value.

Clubhouse Experience

2022 ◽  
pp. 244-264
Author(s):  
Ipek Deveci Kocakoç ◽  
Pınar Özkan
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Supervised Learning ◽  
European Countries ◽  
Media Messages ◽  
The Social ◽  
Social Media Platforms ◽  
Learning By Using

Clubhouse is an invitation-only social networking application that differs from the usual social media platforms in that it is “audio only.” In this chapter, the sentiments in the social media messages about Clubhouse in the classic SMPs are examined by supervised learning (by using Hugging Face Transformer Library), and the user feelings are analyzed. Because Turkey is in the first ranks among European countries in terms of both the number of social media users and the number of messages, the analysis is conducted using the Turkish users. Mentions of Clubhouse have begun on Twitter and Sourtimes platforms in Turkey in early 2021. In this study, the aim is to demonstrate how Clubhouse, a new and different SMP, is evaluated by Twitter and Sourtimes users and to reveal user thoughts about this SMP along the timeline by using sentiment analysis.

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