scholarly journals Combining Cluster-Based Profiling Based on Social Media Features and Association Rule Mining for Personalised Recommendations of Touristic Activities

2021 ◽  
Vol 11 (14) ◽  
pp. 6512
Author(s):  
Jonathan Ayebakuro Orama ◽  
Joan Borràs ◽  
Antonio Moreno
Keyword(s):  
Social Media ◽  
Association Rule ◽  
Association Rule Mining ◽  
Leisure Activities ◽  
User Profiles ◽  
Rule Mining ◽  
Points Of Interest ◽  
The Social ◽  
First Time ◽  
The City

Tourists who visit a city for the first time may find it difficult to decide on places to visit, as the amount of information in the Web about cultural and leisure activities may be large. Recommender systems address this problem by suggesting the points of interest that fit better with the user’s preferences. This paper presents a novel recommender system that leverages tweets to build user profiles, taking into account not only their personal preferences but also their travel habits. Association rules, which are mined from the previous visits of users documented on Twitter, are used to make the final recommendations of places to visit. The system has been applied to data of the city of Barcelona, and the results show that the use of the social media-based clustering procedure increases its performance according to several relevant metrics.

Rare Class Association Rule Mining with Multiple Imbalanced Attributes

2010 ◽  
pp. 66-75 ◽  
Author(s):  
Huaifeng Zhang ◽  
Yanchang Zhao ◽  
Longbing Cao ◽  
Chengqi Zhang ◽  
Hans Bohlscheid
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Uniform Space ◽  
Rule Mining ◽  
Target Class ◽  
Left Hand ◽  
The Social ◽  
Rare Class ◽  
Class Association Rule ◽  
The Right

In this chapter, the authors propose a novel framework for rare class association rule mining. In each class association rule, the right-hand is a target class while the left-hand may contain one or more attributes. This algorithm is focused on the multiple imbalanced attributes on the left-hand. In the proposed framework, the rules with and without imbalanced attributes are processed in parallel. The rules without imbalanced attributes are mined through a standard algorithm while the rules with imbalanced attributes are mined based on newly defined measurements. Through simple transformation, these measurements can be in a uniform space so that only a few parameters need to be specified by user. In the case study, the proposed algorithm is applied in the social security field. Although some attributes are severely imbalanced, rules with a minority of imbalanced attributes have been mined efficiently.

Sentiment Evolution Analysis and Association Rule Mining for COVID-19 Tweets

2021 ◽  
Vol 2 (2) ◽  
pp. 3-21
Author(s):  
Yassine Drias ◽  
Habiba Drias
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Early Stage ◽  
Rule Mining ◽  
Virus Propagation ◽  
The Third ◽  
Evolution Analysis

This article presents a data mining study carried out on social media users in the context of COVID-19 and offers four main contributions. The first one consists in the construction of a COVID-19 dataset composed of tweets posted by users during the first stages of the virus propagation. The second contribution offers a sample of the interactions between users on topics related to the pandemic. The third contribution is a sentiment analysis, which explores the evolution of emotions throughout time, while the fourth one is an association rule mining task. The indicators determined by statistics and the results obtained from sentiment analysis and association rule mining are eloquent. For instance, signs of an upcoming worldwide economic crisis were clearly detected at an early stage in this study. Overall results are promising and can be exploited in the prediction of the aftermath of COVID-19 and similar crisis in the future.

An Association Rule Mining Approach to Discover Demand and Supply Patterns Based on Thai Social Media Data

2021 ◽  
Vol 12 (2) ◽  
pp. 1-16
Author(s):  
Tanatorn Tanantong ◽  
Sarawut Ramjan
Keyword(s):  
Social Media ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Social Media Marketing ◽  
Media Technology ◽  
Rule Mining ◽  
Demand And Supply ◽  
Goods And Services ◽  
Social Media Platforms

In the digital age, social media technology has an important role as a communication platform for interpersonal interactions in the online virtual world. In addition, social media has impacted product exchange behavior in both vendors and buyers, with a shift from the traditional sales model to communication between parties via social media. Social media marketing, an online means of buying, selling, and exchanging goods and services, is increasingly popular due to convenience, speed, and greater choices. This trend has grown rapidly and is set to expand, leading to increased interest in research which analyzes and processes social media marketing data to gain a new integrated body of knowledge to better serve online business transactions. This research covers a new field, which may cause research and development limitations requiring background knowledge in several areas, such as digital technology, data analytics, and business analysis. This research aims to develop a framework to search for association rule mining of demand and supply data on social media platforms. Data is collected from Twitter and underwent cleansing and labeling for separating into five groups. Hashtag data from tweets is then extracted and transformed to input attributes of the framework. Next, association rule mining is performed using the Apriori algorithm in order to determine frequent items and extract candidate association rules. The last stage is rule selection, which uses Twitter-specific statistical attributes, that is, number of retweets and likes, to select highly effective association rules. The findings are that it is possible to mine association rules relating to demand and supply on Twitter. Based on an analysis of the association rule results, the content of those rules reflects trending activities and events at different times. Such information can be analyzed in further research to design improvements in social media marketing.

Choice Modeling of Enterprise Social Media Adoptions

2019 ◽  
Vol 11 (1) ◽  
pp. 12-24
Author(s):  
Chen-Ya Wang ◽  
Hsia-Ching Chang
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Association Rule ◽  
Association Rule Mining ◽  
Fortune 500 ◽  
Econometric Analysis ◽  
Rule Mining ◽  
Enterprise Social Media ◽  
Social Media Platforms ◽  
Dependency Network

To date, many studies focusing on the adoption rates of social media platforms in Fortune 500 firms have been conducted; however, little is known of the adoption time of such platforms, and the relationships between different social media adoptions. This study explores these aspects of social media using a proposed analysis integrating econometric analysis and data mining. Granger causality assists in constructing causal forecasting models of social media adoption time, whereas association rule mining, which can be visualized by dependency network graphs, contributes to understanding hidden relationships among enterprise social media adoption choices. The proposed analysis can account for the unexplained phenomena in a complementary way because different aspects can be drawn from the results of both econometric analysis and data mining.

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