Sentiment analysis of foreign tourists to Bangkok using data mining through online social network

2017 ◽  
Author(s):  
Taweesak Kuhamanee ◽  
Nattaphon Talmongkol ◽  
Krit Chaisuriyakul ◽  
Wimol San-Um ◽  
Noppadon Pongpisuttinun ◽  
...  
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Sentiment Analysis ◽  
Online Social Network ◽  
Using Data

scholarly journals Data mining of social manifestations in Twitter: An ETL approach focused on sentiment analysis

2017 ◽  
Author(s):  
Marcela Yagui ◽  
Luís Maia
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Sentiment Analysis ◽  
Null Hypothesis ◽  
Learning Algorithm ◽  
Online Social Network ◽  
Probabilistic Learning ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Collection Period

The objective of this study was to analyze sentiments of users of online social network twitter to understand how people manifested toward the article published by the magazine Veja on 04-18-16 entitled "bela, recatada e do lar" (beautiful, demure and from home) in an attempt to understand how this behavior evolved in two weeks and to assess which events had aroused greater reaction from people. To this end, a data mining technique known as sentiment analysis was used with the help of the ETL (Extract, Transform and Load) methodology and the Naive Bayes probabilistic learning algorithm. Moreover, the null hypothesis was formulated and tested to see whether two events that took place during the collection period influenced, in fact, the polarity of analyzed sentiments in the generated database.

scholarly journals Adaptive Data Placement for Improving Performance of Online Social Network Services in a Multicloud Environment

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Seunghee Han ◽  
Bosung Kim ◽  
Jaemin Han ◽  
Kyehee Kim ◽  
JooSeok Song
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Data Placement ◽  
Network Services ◽  
Resource Contention ◽  
Redundant Data ◽  
User Data ◽  
Placement Algorithm ◽  
Using Data ◽  
Resource Status

The existing online social network (OSN) services in a multiple-cloud (Multicloud) environment use replications to store user data for improving the service performance. However, it not only generates tremendous traffic for synchronization between data but also stores considerable redundant data, thus causing large storage costs. In addition, it does not provide dynamic load balancing considering the resource status of each cloud. As a result, it cannot cope with the degradation of performance caused by the resource contention. We introduce an adaptive data placement algorithm without the replications for improving the performance of the OSN services in the Multicloud environment. Our approach is designed to avoid server overhead using data balancing technique, which locates data from a cloud to another according to the amount of traffic. To provide acceptable latency delay, it also considers the relationship between users and the distance between user and cloud when transferring data. To validate our approach, we experimented with actual users’ locations and times of use collected from OSN services. Our findings indicate that this approach can reduce the resource contention by an average of more than 59%, reduce storage volume to at least 50%, and maintain the latency delay under 50 ms.

scholarly journals Sentiment Analysis and Summarization of Social Media Content using Topic Modeling

2020 ◽  
Vol 9 (3) ◽  
pp. 209-212
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Opinion Mining ◽  
Text Documents ◽  
Topic Identification ◽  
Key Factor ◽  
Polarity Classification ◽  
Using Data

Explosion of Web 2.0 had made different social media platforms like Facebook, Twitter, Blogs, etc a data hub for the task of Data Mining. Sentiment Analysis or Opinion mining is an automated process of understanding an opinion expressed by customers. By using Data mining techniques, sentiment analysis helps in determining the polarity (Positive, Negative & Neutral) of views expressed by the end user. Nowadays there are terabytes of data available related to any topic then it can be advertising, politics and Survey Companies, etc. CSAT (Customer Satisfaction) is the key factor for this survey companies. In this paper, we used topic modeling by incorporating a LDA algorithm for finding the topics related to social media. We have used datasets of 900 records for analysis. By analysis, we found three important topics from Survey/Response dataset, which are Customers, Agents & Product/Services. Results depict the CSAT score according to Positive, Negative and Neutral response. We used topic modeling which is a statistical modeling technique. Topic modeling is a technique for categorization of text documents into different topics. This approach helps in better summarization of data according to the topic identification and depiction of polarity classification of sentiments expressed.

Cognitive Social Mining Analysis Using Data Mining Techniques

2019 ◽  
pp. 99-117 ◽  
Author(s):  
Dharmpal Singh
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Statistical Modelling ◽  
Data Interpretation ◽  
Computer Mediated ◽  
The Social ◽  
Using Data ◽  
Collaborative Activities

Social media are based on computer-mediated technologies that smooth the progress of the creation and distribution of information, thoughts, idea, career benefits and other forms of expression via implicit communities and networks. The social network analysis (SNA) has emerged with the increasing popularity of social networking services like Facebook, Twitter, etc. Therefore, information about group cohesion, contribution in activities, and associations among subjects can be obtained from the analysis of the blogs. The analysis of the blogs required well-known knowledge discovery tools to help the administrator to discover participant collaborative activities or patterns with inferences to improve the learning and sharing process. Therefore, the goal of this chapter is to provide the data mining tools for information retrieval, statistical modelling and machine learning to employ data pre-processing, data analysis, and data interpretation processes to support the use of social network analysis (SNA) to improve the collaborative activities for better performance.

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