scholarly journals Stayhome Hashtag: Sentiment Analysis on Twitter During the Covid-19 Pandemic

2020 ◽  
Vol 16 (34) ◽  
Author(s):  
Ugur Gunduz
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Disaster Management ◽  
Semantic Analysis ◽  
Global Epidemic ◽  
Social Media Platforms ◽  
The Difference ◽  
Textual Content ◽  
The Impact ◽  
Media Data

With developing technology today, social media has entered every area of our lives. Many people come together and share in social media platforms without time and space restrictions. Social media has been in our lives so much lately. It is an undeniable fact that global outbreaks, which constitute an important part of our lives, are also affected by these networks and that they exist in these networks and share the users. The purpose of making this hashtag analysis is to reveal the difference in discourse and language while analyzing twitter data, while doing this, to evaluate the effects of a global epidemic crisis on language, message and crisis management with social media data. Sentiment analysis of tweets, on the other hand, objectives to take a look at the contents of these messages, to degree the feelings and feelings conveyed. This form of analysis is typically completed through amassing textual content data, then investigating the “sentiment” conveyed. Within the scope of our study, one hundred thousand twitter messages posted with the #stayhome hashtag between 23 May 2020 and 29 May 2020 were examined. The impact and reliability of social media in disaster management could be questioned by carrying out a content analysis based totally on the semantic analysis of the messages given on the Twitter posts with the phrases and frequencies used. Social media and Twitter content are increasingly more identified as treasured resources of public health signals concerning use in ailment surveillance and health disaster management.

A Sentiment Analysis and Role of Twitter for Health Communications

2022 ◽  
pp. 188-205
Author(s):  
Erkan Çiçek ◽  
Uğur Gündüz
Keyword(s):  
Social Media ◽  
Crisis Management ◽  
Semantic Analysis ◽  
Social Media Data ◽  
Global Pandemic ◽  
The Difference ◽  
Textual Content ◽  
The Impact ◽  
Media Data

Social media has been in our lives so much lately that it is an undeniable fact that global pandemics, which constitute an important part of our lives, are also affected by these networks and that they exist in these networks and share the users. The purpose of making this hashtag analysis is to reveal the difference in discourse and language while analyzing Twitter data and to evaluate the effects of a global pandemic crisis on language, message, and crisis management with social media data. This form of analysis is typically completed through amassing textual content data then investigating the “sentiment” conveyed. Within the scope of the study, 11,300 Twitter messages posted with the #stayhome hashtag between 30 May 2020 and 6 June 2020 were examined. The impact and reliability of social media in disaster management could be questioned by carrying out a content analysis based totally on the semantic analysis of the messages given on the Twitter posts with the phrases and frequencies used.

scholarly journals Mining social media data to investigate patient perceptions regarding DMARD pharmacotherapy for rheumatoid arthritis

2020 ◽  
Vol 79 (11) ◽  
pp. 1432-1437 ◽  
Author(s):  
Chanakya Sharma ◽  
Samuel Whittle ◽  
Pari Delir Haghighi ◽  
Frada Burstein ◽  
Roee Sa'adon ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Social Media ◽  
Side Effects ◽  
Sentiment Analysis ◽  
Computer Algorithms ◽  
Social Media Platforms ◽  
Synthetic Agents ◽  
Positive Sentiment ◽  
Negative Sentiment ◽  
Media Data

ObjectivesWe hypothesise that patients have a positive sentiment regarding biological/targeted synthetic disease modifying anti-rheumatic drugs (b/tsDMARDs) and a negative sentiment towards conventional synthetic agents (csDMARDs). We analysed discussions on social media platforms regarding DMARDs to understand the collective sentiment expressed towards these medications.MethodsTreato analytics were used to download all available posts on social media about DMARDs in the context of rheumatoid arthritis. Strict filters ensured that user generated content was downloaded. The sentiment (positive or negative) expressed in these posts was analysed for each DMARD using sentiment analysis. We also analysed the reason(s) for this sentiment for each DMARD, looking specifically at efficacy and side effects.ResultsComputer algorithms analysed millions of social media posts and included 54 742 posts about DMARDs. We found that both classes had an overall positive sentiment. The ratio of positive to negative posts was higher for b/tsDMARDs (1.210) than for csDMARDs (1.048). Efficacy was the most commonly mentioned reason in posts with a positive sentiment and lack of efficacy was the most commonly mentioned reason for a negative sentiment. These were followed by the presence/absence of side effects in negative or positive posts, respectively.ConclusionsPublic opinion on social media is generally positive about DMARDs. Lack of efficacy followed by side effects were the most common themes in posts with a negative sentiment. There are clear reasons why a DMARD generates a positive or negative sentiment, as the sentiment analysis technology becomes more refined, targeted studies could be done to analyse these reasons and allow clinicians to tailor DMARDs to match patient needs.

Social Media to Social Media Analytics

2019 ◽  
Vol 10 (2) ◽  
pp. 57-70 ◽  
Author(s):  
Vikas Kumar ◽  
Pooja Nanda
Keyword(s):  
Social Media ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Ethical Concerns ◽  
Ethical Implications ◽  
The Social ◽  
Social Media Platforms ◽  
The Individual ◽  
The Impact ◽  
Media Data

With the amplification of social media platforms, the importance of social media analytics has exponentially increased for many brands and organizations across the world. Tracking and analyzing the social media data has been contributing as a success parameter for such organizations, however, the data is being poorly harnessed. Therefore, the ethical implications of social media analytics need to be identified and explored for both the organizations and targeted users of social media data. The present work is an exploratory study to identify the various techno-ethical concerns of social media engagement, as well as social media analytics. The impact of these concerns on the individuals, organizations, and society as a whole are discussed. Ethical engagement for the most common social media platforms has been outlined with a number of specific examples to understand the prominent techno-ethical concerns. Both the individual and organizational perspectives have been taken into account to identify the implications of social media analytics.

scholarly journals Twitter Analyzer—How to Use Semantic Analysis to Retrieve an Atmospheric Image around Political Topics in Twitter

2019 ◽  
Vol 3 (3) ◽  
pp. 38 ◽  
Author(s):  
Stefan Spettel ◽  
Dimitrios Vagianos
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Open Source ◽  
Semantic Analysis ◽  
Time Window ◽  
Data Set ◽  
Social Media Data ◽  
Open Source Tool ◽  
Implementation Model ◽  
Media Data

Social media are heavily used to shape political discussions. Thus, it is valuable for corporations and political parties to be able to analyze the content of those discussions. This is exemplified by the work of Cambridge Analytica, in support of the 2016 presidential campaign of Donald Trump. One of the most straightforward metrics is the sentiment of a message, whether it is considered as positive or negative. There are many commercial and/or closed-source tools available which make it possible to analyze social media data, including sentiment analysis (SA). However, to our knowledge, not many publicly available tools have been developed that allow for analyzing social media data and help researchers around the world to enter this quickly expanding field of study. In this paper, we provide a thorough description of implementing a tool that can be used for performing sentiment analysis on tweets. In an effort to underline the necessity for open tools and additional monitoring on the Twittersphere, we propose an implementation model based exclusively on publicly available open-source software. The resulting tool is capable of downloading Tweets in real-time based on hashtags or account names and stores the sentiment for replies to specific tweets. It is therefore capable of measuring the average reaction to one tweet by a person or a hashtag, which can be represented with graphs. Finally, we tested our open-source tool within a case study based on a data set of Twitter accounts and hashtags referring to the Syrian war, covering a short time window of one week in the spring of 2018. The results show that while high accuracy of commercial or other complicated tools may not be achieved, our proposed open source tool makes it possible to get a good overview of the overall replies to specific tweets, as well as a practical perception of tweets, related to specific hashtags, identifying them as positive or negative.

scholarly journals Machine Learning-Based Data Mining Method for Sentiment Analysis of the Sewol Ferry Disaster's Effect on Social Stress

2020 ◽  
Vol 11 ◽  
Author(s):  
Min-Joon Lee ◽  
Tae-Ro Lee ◽  
Seo-Joon Lee ◽  
Jin-Soo Jang ◽  
Eung Ju Kim
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Social Stress ◽  
Scientific Evidence ◽  
National Level ◽  
Psychological Impact ◽  
Social Media Platforms ◽  
Negative Sentiment ◽  
Media Data

The Sewol Ferry Disaster which took place in 16th of April, 2014, was a national level disaster in South Korea that caused severe social distress nation-wide. No research at the domestic level thus far has examined the influence of the disaster on social stress through a sentiment analysis of social media data. Data extracted from YouTube, Twitter, and Facebook were used in this study. The population was users who were randomly selected from the aforementioned social media platforms who had posted texts related to the disaster from April 2014 to March 2015. ANOVA was used for statistical comparison between negative, neutral, and positive sentiments under a 95% confidence level. For NLP-based data mining results, bar graph and word cloud analysis as well as analyses of phrases, entities, and queries were implemented. Research results showed a significantly negative sentiment on all social media platforms. This was mainly related to fundamental agents such as ex-president Park and her related political parties and politicians. YouTube, Twitter, and Facebook results showed negative sentiment in phrases (63.5, 69.4, and 58.9%, respectively), entity (81.1, 69.9, and 76.0%, respectively), and query topic (75.0, 85.4, and 75.0%, respectively). All results were statistically significant (p < 0.001). This research provides scientific evidence of the negative psychological impact of the disaster on the Korean population. This study is significant because it is the first research to conduct sentiment analysis of data extracted from the three largest existing social media platforms regarding the issue of the disaster.

scholarly journals SMAFED: Real-Time Event Detection in Social Media Streams

2021 ◽  
Author(s):  
Taiwo Kolajo ◽  
Olawande Daramola ◽  
Ayodele A. Adebiyi
Keyword(s):  
Social Media ◽  
Event Detection ◽  
Semantic Processing ◽  
Semantic Analysis ◽  
Semantic Representation ◽  
Semantic Relatedness ◽  
Media Analysis ◽  
Social Media Platforms ◽  
Media Stream ◽  
The Impact

Abstract Interactions via social media platforms have made it possible for anyone, irrespective of physical location, to gain access to quick information on events taking place all over the globe. However, the semantic processing of social media data is complicated due to challenges such as language complexity, unstructured data, and ambiguity. In this paper, we proposed the Social Media Analysis Framework for Event Detection (SMAFED). SMAFED aims to facilitate improved semantic analysis of noisy terms in social media streams, improved representation/embedding of social media stream content, and improved summarisation of event clusters in social media streams. For this, we employed key concepts such as integrated knowledge base, resolving ambiguity, semantic representation of social media streams, and Semantic Histogram-based Incremental Clustering based on semantic relatedness. Two evaluation experiments were conducted to validate the approach. First, we evaluated the impact of the data enrichment layer of SMAFED. We found that SMAFED outperformed other pre-processing frameworks with a lower loss function of 0.15 on the first dataset and 0.05 on the second dataset. Secondly, we determined the accuracy of SMAFED at detecting events from social media streams. The result of this second experiment showed that SMAFED outperformed existing event detection approaches with better Precision (0.922), Recall (0.793), and F-Measure (0.853) metric scores. The findings of the study present SMAFED as a more efficient approach to event detection in social media.

scholarly journals Public Perception of Autonomous Mobility Using ML-Based Sentiment Analysis over Social Media Data

Logistics ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 12 ◽  
Author(s):  
Nikolaos Bakalos ◽  
Nikolaos Papadakis ◽  
Antonios Litke
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Public Perception ◽  
Analysis Tool ◽  
Social Media Platforms ◽  
Realistic View ◽  
Application Programming ◽  
Autonomous Mobility ◽  
Programming Interfaces ◽  
Media Data

The purpose of this article is to present a framework for capturing and analyzing social media posts using a sentiment analysis tool to determine the views of the general public towards autonomous mobility. The paper presents the systems used and the results of this analysis, which was performed on social media posts from Twitter and Reddit. To achieve this, a specialized lexicon of terms was used to query social media content from the dedicated application programming interfaces (APIs) that the aforementioned social media platforms provide. The captured posts were then analyzed using a sentiment analysis framework, developed using state-of-the-art deep machine learning (ML) models. This framework provides labeling for the captured posts based on their content (i.e., classifies them as positive or negative opinions). The results of this classification were used to identify fears and autonomous mobility aspects that affect negative opinions. This method can provide a more realistic view of the general public’s perception of automated mobility, as it has the ability to analyze thousands of opinions and encapsulate the users’ opinion in a semi-automated way.

scholarly journals Socio-Analyzer: A Sentiment Analysis Using Social Media Data

10.29007/kzk1 ◽  
2019 ◽  
Author(s):  
Ajay Bandi ◽  
Aziz Fellah
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Weather Data ◽  
Social Changes ◽  
Stream Data ◽  
Social Media Data ◽  
Societal Changes ◽  
The Impact ◽  
Media Data ◽  
Day By Day

The usage of social media is rapidly increasing day by day. The impact of societal changes is bending towards the peoples’ opinions shared on social media. Twitter has re- ceived much attention because of its real-time nature. We investigate recent social changes in MeToo movement by developing Socio-Analyzer. We used our four-phase approach to implement Socio-Analyzer. A total of 393,869 static and stream data is collected from the data world website and analyzed using a classifier. The classifier identify and categorize the data into three categories (positive, neutral, and negative). Our results showed that the maximum peoples’ opinion is neutral. The next higher number of peoples’ opinion is contrary and compared the results with TextBlob. We validate the 765 tweets of weather data and generalize the results to MeToo data. The precision values of Socio-Analyzer and TextBlob are 70.74% and 72.92%, respectively, when considered neutral tweets as positive.

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.

scholarly journals Embed2Detect: temporally clustered embedded words for event detection in social media

2021 ◽  
Author(s):  
Hansi Hettiarachchi ◽  
Mariam Adedoyin-Olowe ◽  
Jagdev Bhogal ◽  
Mohamed Medhat Gaber
Keyword(s):  
Social Media ◽  
Event Detection ◽  
High Volume ◽  
Detection Methods ◽  
Word Embeddings ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Media Data

AbstractSocial media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

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