scholarly journals Sentiment Analysis of Social Survey Data for Local City Councils

2022 ◽  
Vol 11 (1) ◽  
pp. 7
Author(s):  
Marianna Lepelaar ◽  
Adam Wahby ◽  
Martha Rossouw ◽  
Linda Nikitin ◽  
Kanewa Tibble ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Survey Data ◽  
Smart Cities ◽  
Social Indicators ◽  
Media Analysis ◽  
Social Surveys ◽  
Analysis Techniques ◽  
The Social ◽  
The City

Big data analytics can be used by smart cities to improve their citizens’ liveability, health, and wellbeing. Social surveys and also social media can be employed to engage with their communities, and these can require sophisticated analysis techniques. This research was focused on carrying out a sentiment analysis from social surveys. Data analysis techniques using RStudio and Python were applied to several open-source datasets, which included the 2018 Social Indicators Survey dataset published by the City of Melbourne (CoM) and the Casey Next short survey 2016 dataset published by the City of Casey (CoC). The qualitative nature of the CoC dataset responses could produce rich insights using sentiment analysis, unlike the quantitative CoM dataset. RStudio analysis created word cloud visualizations and bar charts for sentiment values. These were then used to inform social media analysis via the Twitter application programming interface. The R codes were all integrated within a Shiny application to create a set of user-friendly interactive web apps that generate sentiment analysis both from the historic survey data and more immediately from the Twitter feeds. The web apps were embedded within a website that provides a customisable solution to estimate sentiment for key issues. Global sentiment was also compared between the social media approach and the 2016 survey dataset analysis and showed some correlation, although there are caveats on the use of social media for sentiment analysis. Further refinement of the methodology is required to improve the social media app and to calibrate it against analysis of recent survey data.

Technologies Disability and Inclusion

2018 ◽  
Author(s):  
Tomas Brusell
Keyword(s):  
Social Media ◽  
Augmented Reality ◽  
Information And Communication Technologies ◽  
Future Development ◽  
Smart Cities ◽  
Communication Technologies ◽  
Modern Technology ◽  
The Social ◽  
The Disabled ◽  
Information And Communication

When modern technology permeates every corner of life, there are ignited more and more hopes among the disabled to be compensated for the loss of mobility and participation in normal life, and with Information and Communication Technologies (ICT), Exoskeleton Technologies and truly hands free technologies (HMI), it's possible for the disabled to be included in the social and pedagogic spheres, especially via computers and smartphones with social media apps and digital instruments for Augmented Reality (AR) .In this paper a nouvel HMI technology is presented with relevance for the inclusion of disabled in every day life with specific focus on the future development of "smart cities" and "smart homes".

scholarly journals Using Social Media in Tourist Sentiment Analysis: A Case Study of Andalusia during the Covid-19 Pandemic

2021 ◽  
Vol 13 (7) ◽  
pp. 3836
Author(s):  
David Flores-Ruiz ◽  
Adolfo Elizondo-Salto ◽  
María de la O. Barroso-González
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Cost Savings ◽  
Mass Scale ◽  
Tourism Sector ◽  
The Social ◽  
Analytical Tools ◽  
Media Data

This paper explores the role of social media in tourist sentiment analysis. To do this, it describes previous studies that have carried out tourist sentiment analysis using social media data, before analyzing changes in tourists’ sentiments and behaviors during the COVID-19 pandemic. In the case study, which focuses on Andalusia, the changes experienced by the tourism sector in the southern Spanish region as a result of the COVID-19 pandemic are assessed using the Andalusian Tourism Situation Survey (ECTA). This information is then compared with data obtained from a sentiment analysis based on the social network Twitter. On the basis of this comparative analysis, the paper concludes that it is possible to identify and classify tourists’ perceptions using sentiment analysis on a mass scale with the help of statistical software (RStudio and Knime). The sentiment analysis using Twitter data correlates with and is supplemented by information from the ECTA survey, with both analyses showing that tourists placed greater value on safety and preferred to travel individually to nearby, less crowded destinations since the pandemic began. Of the two analytical tools, sentiment analysis can be carried out on social media on a continuous basis and offers cost savings.

Polarity Classification of Arabic Sentiments

2016 ◽  
Vol 11 (3) ◽  
pp. 32-49 ◽  
Author(s):  
Mohammed N. Al-Kabi ◽  
Heider A. Wahsheh ◽  
Izzat M. Alsmadi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Operating Characteristic ◽  
Opinion Mining ◽  
Online Social Network ◽  
The Social ◽  
Polarity Classification ◽  
Arabic Sentiment Analysis ◽  
Modern Standard

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

The Effects of Different Kernels in SVM Sentiment Analysis on Mass Social Distancing

2020 ◽  
Vol 9 (2) ◽  
pp. 161
Author(s):  
Komang Dhiyo Yonatha Wijaya ◽  
Anak Agung Istri Ngurah Eka Karyawati
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Kernel Method ◽  
Idle Time ◽  
Linear Kernel ◽  
Social Distancing ◽  
The Social ◽  
Negative Sentiment ◽  
F Measure ◽  
Kernel Yield

During this pandemic, social media has become a major need as a means of communication. One of the social medias used is Twitter by using messages referred to as tweets. Indonesia currently undergoing mass social distancing. During this time most people use social media in order to spend their idle time However, sometimes, this result in negative sentiment that used to insult and aimed at an individual or group. To filter that kind of tweets, a sentiment analysis was performed with SVM and 3 different kernel method. Tweets are labelled into 3 classes of positive, neutral, and negative. The experiments are conducted to determine which kernel is better. From the sentiment analysis that has been performed, SVM linear kernel yield the best score Some experiments show that the precision of linear kernel is 57%, recall is 50%, and f-measure is 44%

A sentiment analysis system for social media using machine learning techniques: Social enablement

2018 ◽  
Vol 34 (3) ◽  
pp. 569-581 ◽  
Author(s):  
Sujata Rani ◽  
Parteek Kumar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Tool ◽  
Data Set ◽  
Learning Techniques

Abstract In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots.

scholarly journals #ODU2ODU: Testing the Benefits of a Partnership Between Sport Management Classrooms

2020 ◽  
Vol 1 ◽  
pp. 18-35
Author(s):  
Brendan O'Hallarn ◽  
James Strode
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Real World ◽  
Sport Management ◽  
Media Use ◽  
Academic Research ◽  
Perceived Benefits ◽  
Media Technologies ◽  
Social Media Site ◽  
The Social

As sport management pedagogy has evolved, an effort has been made to incorporate popular and innovative social media technologies into classroom instruction. Academic research has suggested how the technology can be utilized to provide real-world skills for students and develop proficiencies in an area where many sport management graduates find employment. Notable among the recommendations about social media use by sport management scholars is a lack of research testing the efficacy of these tools in improving curricula. The current study relied on the recommendations of Sanderson and Browning (2015) to use the social media site Twitter to create online partnerships, testing the perceived benefits of such an arrangement through end-of-semester surveys with student participants. While the survey data show a true partnership may be difficult to realize—particularly during a single semester—the benefits of such an assignment were clearly articulated.

scholarly journals ‘Wife, Mommy, Pastor and Friend’: The Rise of Female Evangelical Microcelebrities

Religions ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 758
Author(s):  
Katie Christine Gaddini
Keyword(s):  
Social Media ◽  
Digital Media ◽  
Media Analysis ◽  
Media Content ◽  
Religious Authority ◽  
Social Media Analysis ◽  
The Social ◽  
Prosperity Theology ◽  
Evangelical Women ◽  
Ethnographic Data

The popularity of digital media has spurred what has been called a “crisis of authority”. How do female evangelical microcelebrities figure in this crisis? Many of these women belong to churches led by male pastors, have amassed a large following online, and are sought-after speakers and teachers. This paper analyses how gender, religious authority, and the digital sphere collide through the rise of female evangelical microcelebrities. Bringing together ethnographic data, textual analysis, and social media analysis of six prominent women, I emphasize the power of representation to impact religious practices and religious meaning. This article examines how evangelical women are performing and negotiating their legitimacy as the Internet and fluid geographical boundaries challenge local models of religious authority. Moving away from a binary perspective of “having” or “not having” authority, this paper considers the various spheres of authority that evangelical microcelebrities occupy, including normative womanhood, prosperity theology, and politics. Finally, by examining the social media content put forth by female evangelical microcelebrities, I interrogate the political stakes of evangelical women’s authority.

scholarly journals Social Media Analysis Using Probabilistic Neural Network Algorithm to Know Personality Traits

Jurnal INFORM ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 61-64
Author(s):  
Mohammad Zoqi Sarwani ◽  
Dian Ahkam Sani
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Probabilistic Neural Network ◽  
Media Analysis ◽  
Training Data ◽  
The Internet ◽  
Accuracy Score ◽  
Network Algorithm ◽  
The Social ◽  
Neural Network Algorithm

The Internet creates a new space where people can interact and communicate efficiently. Social media is one type of media used to interact on the internet. Facebook and Twitter are one of the social media. Many people are not aware of bringing their personal life into the public. So that unconsciously provides information about his personality. Big Five personality is one type of personality assessment method and is used as a reference in this study. The data used is the social media status from both Facebook and Twitter. Status has been taken from 50 social media users. Each user is taken as a text status. The results of tests performed using the Probabilistic Neural Network algorithm obtained an average accuracy score of 86.99% during the training process and 83.66% at the time of testing with a total of 30 training data and 20 test data.

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