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.

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 Analysis of Social Media Users Sentiments against Omnibus Law Based on Hashtags on Twitter

SISTEMASI ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 197
Author(s):  
Okta Fanny ◽  
Heri Suroyo
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Main Topic ◽  
Accuracy Score ◽  
Test Results ◽  
Bayes Classifier ◽  
The Public ◽  
Average Accuracy ◽  
Positive Sentiment ◽  
Negative Sentiment

From the research that has been done, it can be concluded that Sentiment Analysis can be used to know the sentiment of the public, especially Twitter netizens against omnibus law. After the sentiment analysis, it looks neutral artmen with the largest percentage of 55%, then positive sentiment by 35% and negative sentiment by 10%. The results of the analysis showed that the Naïve Bayes Classifier method provides classification test results with accuracy in Hashtag Pro with an average accuracy score of 92.1%, precision values with an average of 94.8% and recall values with an average of 90.7%. While Hashtag Counter For data classification, with an average accuracy value of 98.3%, precision value with an average of 97.6% and recall value with an average of 98.7%. The result of text cloud analysis conducted on a combination of hashtags both Hashtag pros and Hashtags cons, the dominant word appears is Omnibus Law which means that all hashtags in scrap is really discussing the main topic that is about Omnibus Law

scholarly journals Visual Sentiment Analysis on Social Media Data

2021 ◽  
pp. 366-372
Author(s):  
Harshala Bhoir ◽  
K. Jayamalini
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Social Media Data ◽  
Media Image ◽  
The Social ◽  
Textual Data ◽  
Art Works ◽  
Negative Sentiment ◽  
Media Data

Visual sentiment analysis is the way to automatically recognize positive and negative emotions from images, videos, graphics, stickers etc. To estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment, most of the state-of-the-art works exploit the text associated to a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user which usually includes text useful to maximize the diffusion of the social post. Proposed system will extract and employ an Objective Text description of images automatically extracted from the visual content rather than the classic Subjective Text provided by the user. The proposed System will extract three views visual view, subjective text view and objective text view of social media image and will give sentiment polarity positive, negative or neutral based on hypothesis table.

Identifying the Real Differences of Opinion in Social Media Sentiment

2013 ◽  
Vol 55 (6) ◽  
pp. 757-767 ◽  
Author(s):  
Annie Pettit
Keyword(s):  
Social Media ◽  
Older People ◽  
Word Choice ◽  
Correction Factors ◽  
Social Media Data ◽  
Differences Of Opinion ◽  
Positive Sentiment ◽  
Negative Sentiment ◽  
Media Sentiment ◽  
Media Data

This study examined the differences in social media sentiment based on author gender, age and country. After creating ten category-generic datasets, millions of social media verbatims from thousands of websites were collected, cleaned of spam, and scored into five-point sentiment scales. The results showed that women exhibit more positive sentiment, older people exhibit more positive sentiment, and Australians exhibit more positive sentiment, while Americans share more negative sentiment. The differences were small but clear, suggesting that research methodologists should apply correction factors to ensure that their results more accurately reflect differences of opinion as opposed to differences of word choice. Business users of social media data can be reassured that correction factors are not required to improve the accuracy of their research.

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.

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 Pembentukan Dataset Token Sentimen Berdasarkan Akun Instagram Brand Elektronik Menggunakan K-Nearest Neighbors

2021 ◽  
Vol 12 (1) ◽  
pp. 68
Author(s):  
Kristian Adi Nugraha
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Communication Platform ◽  
Social Media Platforms ◽  
Negative Sentiment ◽  
Total Accuracy

Abstract. Generating Sentiment Token Dataset Based on Electronics Brand Instagram Account using K-Nearest Neighbors. Instagram is currently one of the most popular social media platforms for businesses and brand owners to promote their products. Because Instagram is a two-way communication platform, people can respond to any promotional content posted on Instagram. People's reactions come in a variety of form, and frequently include both positive and negative sentiment. This study aims to identify the words used in one type of sentiment, then use the K-NN approach to construct a token dataset by summarizing the phrases in many labels according to the sentiment type. The total accuracy value of the dataset for K = 1 is 33.38% (positive), 59.96% (negative), and 56.60% (neutral) based on the results of the tests performed.Keywords: sentiment analysis, K-Nearest Neighbors, dataset, InstagramAbstrak. Instagram saat ini menjadi salah satu media sosial yang banyak digunakan oleh perusahaan atau pemilik brand untuk melakukan promosi terhadap produk-produk yang dimilikinya. Karena bersifat dua arah, masyarakat dapat memberikan respon terhadap aktivitas promosi yang dilakukan oleh sebuah perusahaan melalui Instagram. Respon dari masyarakat memiliki varian yang beragam dan seringkali mengandung unsur sentimen baik positif maupun negatif. Penelitian ini mencoba untuk mengidentifikasi kata-kata yang digunakan dalam satu jenis sentimen, kemudian membuat dataset token dengan cara merangkum kata-kata tersebut dalam beberapa label sesuai jenis sentimen masing-masing menggunakan metode K-NN. Berdasarkan hasil pengujian yang dilakukan, didapatkan nilai akurasi dari dataset sebesar 33.38% (positif), 59.96% (negatif), dan 56.60% (netral) untuk K = 1.Kata Kunci: analisis sentimen, K-Nearest Neighbors, dataset, Instagram

scholarly journals Entity Profiling to Identify Actor Involvement in Topics of Social Media Content

2020 ◽  
Vol 14 (4) ◽  
pp. 417
Author(s):  
Puji Winar Cahyo ◽  
Muhammad Habibi
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Online News ◽  
Support Vector ◽  
Media Content ◽  
Positive Sentiment ◽  
Negative Sentiment

The efficiency of using social media affected modern society's nature and communication; they are more interested in talking through social media than meeting in the real world. The number of talks on social media content depends on the topic being discussed. The more topic interesting will impact the amount of data on social media will be. The data can be analyzed to get the influence of actors (account mentions) on the conversation. The power of an actor can be measured from how often the actor is mentioned in the conversation. This paper aims to conduct entity profiling on social media content to analyze an actor's influence on discussion. Furthermore, using sentiment analysis can determine the sentiment about an actor from a conversation topic. The Latent Dirichlet Allocation (LDA) method is used for analyzes topic modeling, while the Support Vector Machine (SVM) is used for sentiment analysis. This research can show that topics with positive sentiment are more likely to be involved in disaster management accounts, while topics with negative sentiment are more towards involvement in politicians, critics, and online news.

Social media sentiment analysis: lexicon versus machine learning

2017 ◽  
Vol 34 (6) ◽  
pp. 480-488 ◽  
Author(s):  
Chedia Dhaoui ◽  
Cynthia M. Webster ◽  
Lay Peng Tan
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Learning Approaches ◽  
Combined Approach ◽  
Content Type ◽  
Positive Sentiment ◽  
Improved Performance ◽  
Automated Tools ◽  
Negative Sentiment

Purpose With the soaring volumes of brand-related social media conversations, digital marketers have extensive opportunities to track and analyse consumers’ feelings and opinions about brands, products or services embedded within consumer-generated content (CGC). These “Big Data” opportunities render manual approaches to sentiment analysis impractical and raise the need to develop automated tools to analyse consumer sentiment expressed in text format. This paper aims to evaluate and compare the performance of two prominent approaches to automated sentiment analysis applied to CGC on social media and explores the benefits of combining them. Design/methodology/approach A sample of 850 consumer comments from 83 Facebook brand pages are used to test and compare lexicon-based and machine learning approaches to sentiment analysis, as well as their combination, using the LIWC2015 lexicon and RTextTools machine learning package. Findings Results show the two approaches are similar in accuracy, both achieving higher accuracy when classifying positive sentiment than negative sentiment. However, they differ substantially in their classification ensembles. The combined approach demonstrates significantly improved performance in classifying positive sentiment. Research limitations/implications Further research is required to improve the accuracy of negative sentiment classification. The combined approach needs to be applied to other kinds of CGCs on social media such as tweets. Practical implications The findings inform decision-making around which sentiment analysis approaches (or a combination thereof) is best to analyse CGC on social media. Originality/value This study combines two sentiment analysis approaches and demonstrates significantly improved performance.

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.

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