Subjective Text Mining for Arabic Social Media

2017 ◽  
Vol 13 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Nourah F. Bin Hathlian ◽  
Alaaeldin M. Hafez
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Research Area ◽  
Arabic Text ◽  
Classification Methods ◽  
Learning Mechanisms ◽  
Subjective Analysis ◽  
Related Information ◽  
Data Source ◽  
Rich Content

The need for designing Arabic text mining systems for the use on social media posts is increasingly becoming a significant and attractive research area. It serves and enhances the knowledge needed in various domains. The main focus of this paper is to propose a novel framework combining sentiment analysis with subjective analysis on Arabic social media posts to determine whether people are interested or not interested in a defined subject. For those purposes, text classification methods—including preprocessing and machine learning mechanisms—are applied. Essentially, the performance of the framework is tested using Twitter as a data source, where possible volunteers on a certain subject are identified based on their posted tweets along with their subject-related information. Twitter is considered because of its popularity and its rich content from online microblogging services. The results obtained are very promising with an accuracy of 89%, thereby encouraging further research.

Subjective Text Mining for Arabic Social Media

2020 ◽  
pp. 1483-1495
Author(s):  
Nourah F. Bin Hathlian ◽  
Alaaeldin M. Hafez
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Text Classification ◽  
Research Area ◽  
Classification Methods ◽  
Learning Mechanisms ◽  
Subjective Analysis ◽  
Related Information ◽  
Data Source ◽  
Rich Content

The need for designing Arabic text mining systems for the use on social media posts is increasingly becoming a significant and attractive research area. It serves and enhances the knowledge needed in various domains. The main focus of this paper is to propose a novel framework combining sentiment analysis with subjective analysis on Arabic social media posts to determine whether people are interested or not interested in a defined subject. For those purposes, text classification methods—including preprocessing and machine learning mechanisms—are applied. Essentially, the performance of the framework is tested using Twitter as a data source, where possible volunteers on a certain subject are identified based on their posted tweets along with their subject-related information. Twitter is considered because of its popularity and its rich content from online microblogging services. The results obtained are very promising with an accuracy of 89%, thereby encouraging further research.

scholarly journals Bag-of-Phrases (BoPh) and sentiment analysis of Arabic text in Twitter

2020 ◽  
Vol 13 (40) ◽  
pp. 4202-4215
Author(s):  
Hamoud H Alshammari
Keyword(s):  
Text Mining ◽  
Sentiment Analysis ◽  
Arabic Language ◽  
Training Data ◽  
Main Role ◽  
Arabic Text ◽  
Manual Annotation ◽  
True Negative ◽  
Data Set ◽  
Subjective Analysis

Background/Objectives: Sentiment analysis plays main role in various text mining problems. Although, the Arabic text mining is important especially in the field of sentiment analysis, there is a paucity of research in it, especially, when it plays an important role in different issues in Arabic countries. Arabic language has many dialects that people use to express their feelings in social media. The objective of this study is to perform an experiment that follow the subjective opinion from the text. Subjective Analysis is one way that we can implement to improve the accuracy of the sentiment results in such texts in some dialects, that hide various meanings behind the words such as Saudi dialect. Methods/Statistical analysis: In this study, we manually annotated more than 8,000 tweets to have training and testing data sets with positive or negative words and phrases. Then we proposed a “Bag of Phrases” methodology to analyze the sentiments in the texts, which helped to improve the performance of sentiment analysis. Since using bag of words method is not enough in many cases, we applied a Naive Bayes algorithm to test our method. Findings: The results show that the accuracy of having True positive or True negative is about 84% comparing by using manual annotation process. The accuracy is calculated after taking into consideration the margin of error due to the manual annotation step and subjective interpretation of the texts by the annotators. Novelty/Applications: The novelty of the study is having more accurate training data set comparing with the other works in Saudi dialect for Arabic text, and proposing the BoPh concept.

scholarly journals Aplikasi Text Mining untuk Klasterisasi Aduan Masyarakat Kota Semarang Menggunakan Algoritma K-means

2021 ◽  
Vol 18 (2) ◽  
pp. 215
Author(s):  
Dita Afida ◽  
Erika Devi Udayanti ◽  
Etika Kartikadarma
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Clustering Algorithm ◽  
City Government ◽  
Community Based ◽  
Text Data ◽  
Government Organizations ◽  
Text Document ◽  
Data Source ◽  
Mining Methods

<p>Social media is a service that is very supportive for government activities, especially in providing openness and community-based government. One form of its implementation is the Semarang City government through the Center for Community Complaints Management (P3M), whose task is to manage community complaints that enter one of the communication channels namely social media twitter. The number of public complaints that enter every day is very varied. This is certainly quite difficult for managers in categorizing complaints reports according to the relevant Local Government Organizations (OPD). This paper focuses on the problem of how to conduct clustering of community complaints. The data source comes from Twitter using the keyword "Laporhendi". Text document data from community complaint tweets was analyzed by text mining methods. A number of pre-processing of text data processing begins with the process of case folding, tokenizing, stemming, stopword removal and word robbering with tf-idf. In conducting cluster mapping, clustering algorithm will be used in dividing the complaint cluster, namely the k-means algorithm. Evaluation of cluster results is done by using purity to determine the accuracy of the results of grouping or clustering.</p>

scholarly journals Classification of Arabic Tweets: A Review

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1143
Author(s):  
Meshrif Alruily
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Middle Ages ◽  
Text Classification ◽  
Text Processing ◽  
Learning Algorithms ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Arabic Text

Text classification is a prominent research area, gaining more interest in academia, industry and social media. Arabic is one of the world’s most famous languages and it had a significant role in science, mathematics and philosophy in Europe in the middle ages. During the Arab Spring, social media, that is, Facebook, Twitter and Instagram, played an essential role in establishing, running, and spreading these movements. Arabic Sentiment Analysis (ASA) and Arabic Text Classification (ATC) for these social media tools are hot topics, aiming to obtain valuable Arabic text insights. Although some surveys are available on this topic, the studies and research on Arabic Tweets need to be classified on the basis of machine learning algorithms. Machine learning algorithms and lexicon-based classifications are considered essential tools for text processing. In this paper, a comparison of previous surveys is presented, elaborating the need for a comprehensive study on Arabic Tweets. Research studies are classified according to machine learning algorithms, supervised learning, unsupervised learning, hybrid, and lexicon-based classifications, and their advantages/disadvantages are discussed comprehensively. We pose different challenges and future research directions.

Infoveillance based on Social Sensors to Analyze the impact of Covid19 in South American Population (Preprint)

2020 ◽  
Author(s):  
Josimar E. Chire Saire
Keyword(s):  
Text Mining ◽  
Electronic Communication ◽  
Spanish Speakers ◽  
South American ◽  
South American Population ◽  
The People ◽  
Data Source ◽  
The Impact ◽  
General Concern ◽  
Social Sensors

BACKGROUND Infoveillance is an application from Infodemiology field with the aim to monitor public health and create public policies. Social sensor is the people providing thought, ideas through electronic communication channels(i.e. Internet). The actual scenario is related to tackle the covid19 impact over the world, many countries have the infrastructure, scientists to help the growth and countries took actions to decrease the impact. South American countries have a different context about Economy, Health and Research, so Infoveillance can be a useful tool to monitor and improve the decisions and be more strategical. The motivation of this work is analyze the capital of Spanish Speakers Countries in South America using a Text Mining Approach with Twitter as data source. The preliminary results helps to understand what happens two weeks ago and opens the analysis from different perspectives i.e. Economics, Social. OBJECTIVE Analyze the behaviour of South American Capitals in front of covid19 pandemics and show the helpfulness of Text Mining Approach for Infoveillance tasks. METHODS Text Mining process RESULTS - Argentina and Venezuela capitals are the biggest number of post during this period, opposite with Bolivia, Ecuador and Uruguay. - Most relevant users are related to mass media like radio, television or newspapers. - There is a general concern about covid19 but every country talks about different areas: Economics, Health, Environmental Impact. CONCLUSIONS Infoveillance based on Social Sensors with data coming from Twitter can help to understand the trends on the population of the capitals. Besides, it is necessary to filter the posts for processing the text and get insights about frequency, top users, most important terms. This data is useful to analyse the population from different approaches. INTERNATIONAL REGISTERED REPORT RR2-https://doi.org/10.1101/2020.04.06.20055749

“Tradition,” Vernacularism, and Learning to Be a Folk Musician With Social Media

2020 ◽  
pp. 442-466
Author(s):  
Simon Keegan-Phipps ◽  
Lucy Wright
Keyword(s):  
Social Media ◽  
Digital Media ◽  
Case Studies ◽  
Folk Music ◽  
Learning Mechanisms ◽  
Media Technologies ◽  
Arts And Humanities ◽  
The Arts ◽  
The Social ◽  
Social Shaping

This chapter considers the role of social media (broadly conceived) in the learning experiences of folk musicians in the Anglophone West. The chapter draws on the findings of the Digital Folk project, funded by the Arts and Humanities Research Council (UK), and begins by summarizing and problematizing the nature of learning as a concept in the folk music context. It briefly explicates the instructive, appropriative, and locative impacts of digital media for folk music learning before exploring in detail two case studies of folk-oriented social media: (1) the phenomenon of abc notation as a transmissive media and (2) the Mudcat Café website as an example of the folk-oriented discussion forum. These case studies are shown to exemplify and illuminate the constructs of traditional transmission and vernacularism as significant influences on the social shaping and deployment of folk-related media technologies. The chapter concludes by reflecting on the need to understand the musical learning process as a culturally performative act and to recognize online learning mechanisms as sites for the (re)negotiation of musical, cultural, local, and personal identities.

scholarly journals Quantifying Media Effects, Its Content, and Role in Promoting Community Awareness of Chikungunya Epidemic in Bangladesh

Epidemiologia ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 84-94
Author(s):  
Mst. Marium Begum ◽  
Osman Ulvi ◽  
Ajlina Karamehic-Muratovic ◽  
Mallory R. Walsh ◽  
Hasan Tarek ◽  
...  
Keyword(s):  
Social Media ◽  
Web Based ◽  
Study Objective ◽  
The Public ◽  
Mainstream Media ◽  
Related Information ◽  
Vector Borne ◽  
Media Outlet ◽  
And Control

Background: Chikungunya is a vector-borne disease, mostly present in tropical and subtropical regions. The virus is spread by Ae. aegypti and Ae. albopictus mosquitos and symptoms include high fever to severe joint pain. Dhaka, Bangladesh, suffered an outbreak of chikungunya in 2017 lasting from April to September. With the goal of reducing cases, social media was at the forefront during this outbreak and educated the public about symptoms, prevention, and control of the virus. Popular web-based sources such as the top dailies in Bangladesh, local news outlets, and Facebook spread awareness of the outbreak. Objective: This study sought to investigate the role of social and mainstream media during the chikungunya epidemic. The study objective was to determine if social media can improve awareness of and practice associated with reducing cases of chikungunya. Methods: We collected chikungunya-related information circulated from the top nine television channels in Dhaka, Bangladesh, airing from 1st April–20th August 2017. All the news published in the top six dailies in Bangladesh were also compiled. The 50 most viewed chikungunya-related Bengali videos were manually coded and analyzed. Other social media outlets, such as Facebook, were also analyzed to determine the number of chikungunya-related posts and responses to these posts. Results: Our study showed that media outlets were associated with reducing cases of chikungunya, indicating that media has the potential to impact future outbreaks of these alpha viruses. Each media outlet (e.g., web, television) had an impact on the human response to an individual’s healthcare during this outbreak. Conclusions: To prevent future outbreaks of chikungunya, media outlets and social media can be used to educate the public regarding prevention strategies such as encouraging safe travel, removing stagnant water sources, and assisting with tracking cases globally to determine where future outbreaks may occur.

scholarly journals A computational reward learning account of social media engagement

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Björn Lindström ◽  
Martin Bellander ◽  
David T. Schultner ◽  
Allen Chang ◽  
Philippe N. Tobler ◽  
...  
Keyword(s):  
Social Media ◽  
Human Behavior ◽  
Computational Models ◽  
Average Rate ◽  
Human Life ◽  
Modern Human ◽  
Reward Learning ◽  
Learning Mechanisms ◽  
Social Media Engagement ◽  
Social Rewards

AbstractSocial media has become a modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (likes), portraying the online world as a Skinner Box for the modern human. Yet despite such portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. Here, we apply a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyze over one million posts from over 4000 individuals on multiple social media platforms, using computational models based on reinforcement learning theory. Our results consistently show that human behavior on social media conforms qualitatively and quantitatively to the principles of reward learning. Specifically, social media users spaced their posts to maximize the average rate of accrued social rewards, in a manner subject to both the effort cost of posting and the opportunity cost of inaction. Results further reveal meaningful individual difference profiles in social reward learning on social media. Finally, an online experiment (n = 176), mimicking key aspects of social media, verifies that social rewards causally influence behavior as posited by our computational account. Together, these findings support a reward learning account of social media engagement and offer new insights into this emergent mode of modern human behavior.

Effectiveness of Travel Social Media Influencers: A Case of Eco-Friendly Hotels

2021 ◽  
pp. 004728752110194
Author(s):  
Payal S. Kapoor ◽  
M. S. Balaji ◽  
Yangyang Jiang ◽  
Charles Jebarajakirthy
Keyword(s):  
Social Media ◽  
Federal Trade Commission ◽  
Service Providers ◽  
Digital Marketing ◽  
Intention To Stay ◽  
Argument Quality ◽  
Related Information ◽  
Sustainability Practices ◽  
Tourism Service ◽  
Primary Channel

With social media becoming the primary channel for travelers to acquire travel-related information, tourism service providers are increasingly partnering with social media influencers (SMIs) as part of their digital marketing strategy. The present study investigates the effectiveness of SMIs by examining the role that two message factors—argument quality and sponsorship status—have on travelers’ perceptions of a hotel’s commitment to sustainability and their intention to stay at the hotel. Results from four studies show that when eco-friendly hotels sponsor SMIs, an attribute-value message is more effective than a simple recommendation message in influencing travelers’ perceptions and intentions. Given the latest Federal Trade Commission regulations regarding sponsorship disclosure practices, the findings offer valuable insights for tourism providers using SMIs. The study findings suggest that SMIs should create sponsored messages that provide rational and objective information about the hotel’s sustainability practices to stimulate travelers’ related cognitions and persuade them to patronize the hotel.

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