Human-annotated dataset for social media sentiment analysis for Albanian language
Ontology highlight
ABSTRACT:
SUBMITTER: Kadriu F
PROVIDER: S-EPMC9272335 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The Bosnian language holds significant importance as a member of the West-South Slavic subgroup within the Slavic branch of the Indo-European linguistic family. With approximately 2.5 million speakers in Europe, including 1.87 million individuals in Bosnia and Herzegovina alone, the Bosnian language constitutes the mother tongue for a considerable portion of the population. In Natural Language Processing (NLP) tasks related to the Bosnian language, besides removing stop words, it is important to consider the influence of other linguistic elements. Bosnian text contains words derived from diminishers, relative intensifiers, minimizers, maximizers, boosters, and approximators. These words contribute to the overall meaning and sentiment analysis of the text. By including these elements in NLP models and algorithms, researchers can achieve more accurate and nuanced analysis of Bosnian language data, enhancing the effectiveness of NLP applications. The two lists of sentiment annotated words that present the core of the Bosnian sentiment-annotated lexicon, a list of the stopwords, and a list of Affirmative and non-Affrimative words (AnAwords) composed mostly of intensifiers and diminishers, were used to construct a dataset that presents the base for sentiment analysis in the Bosnian language.
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Project description:This paper presents dataset collected from social networks that are mostly used by youth of Commonwealth of Independent States (CIS) countries. The data was collected from public accounts of VKontakte social network by using VK.api and applying the most used keywords that would signify depressive mood. The collected data was classified by psychologists into two types: depressive and non-depressive. The dataset consists of 32 018 depressive posts and 32 021 non-depressive posts. Since the most common language that is spoken in CIS countries is Russian, the posts are written in Russian, consequently the collected data is in Russian language as well. The data can mostly be useful for researchers who explore tendencies to depression in CIS countries. The dataset is important for the research community, as it was not only collected from open sources, but also marked by our psychiatrists from the republican scientific and practical center of mental health. Since the dataset has very high validity, it can be used for further research in the field of mental health.
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Project description:ObjectiveTo examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information from highly shared websites of Twitter users in the United States; and to assist public health communication of vaccines.BackgroundVaccine hesitancy continues to contribute to suboptimal vaccination coverage in the United States, posing significant risk of disease outbreaks, yet remains poorly understood.MethodsWe constructed semantic networks of vaccine information from internet articles shared by Twitter users in the United States. We analyzed resulting network topology, compared semantic differences, and identified the most salient concepts within networks expressing positive, negative, and neutral vaccine sentiment.ResultsThe semantic network of positive vaccine sentiment demonstrated greater cohesiveness in discourse compared to the larger, less-connected network of negative vaccine sentiment. The positive sentiment network centered around parents and focused on communicating health risks and benefits, highlighting medical concepts such as measles, autism, HPV vaccine, vaccine-autism link, meningococcal disease, and MMR vaccine. In contrast, the negative network centered around children and focused on organizational bodies such as CDC, vaccine industry, doctors, mainstream media, pharmaceutical companies, and United States. The prevalence of negative vaccine sentiment was demonstrated through diverse messaging, framed around skepticism and distrust of government organizations that communicate scientific evidence supporting positive vaccine benefits.ConclusionSemantic network analysis of vaccine sentiment in online social media can enhance understanding of the scope and variability of current attitudes and beliefs toward vaccines. Our study synthesizes quantitative and qualitative evidence from an interdisciplinary approach to better understand complex drivers of vaccine hesitancy for public health communication, to improve vaccine confidence and vaccination coverage in the United States.
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Project description:As the influence and risk of infectious diseases increase, efforts are being made to predict the number of confirmed infectious disease patients, but research involving the qualitative opinions of social media users is scarce. However, social data can change the psychology and behaviors of crowds through information dissemination, which can affect the spread of infectious diseases. Existing studies have used the number of confirmed cases and spatial data to predict the number of confirmed cases of infectious diseases. However, studies using opinions from social data that affect changes in human behavior in relation to the spread of infectious diseases are inadequate. Therefore, herein, we propose a new approach for sentiment analysis of social data by using opinion mining and to predict the number of confirmed cases of infectious diseases by using machine learning techniques. To build a sentiment dictionary specialized for predicting infectious diseases, we used Word2Vec to expand the existing sentiment dictionary and calculate the daily sentiment polarity by dividing it into positive and negative polarities from collected social data. Thereafter, we developed an algorithm to predict the number of confirmed infectious patients by using both positive and negative polarities with DNN, LSTM and GRU. The method proposed herein showed that the prediction results of the number of confirmed cases obtained using opinion mining were 1.12% and 3% better than those obtained without using opinion mining in LSTM and GRU model, and it is expected that social data will be used from a qualitative perspective for predicting the number of confirmed cases of infectious diseases.
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Project description:Despite the popularity and efficiency of dictionary-based sentiment analysis (DSA) for public health research, limited empirical evidence has been produced about the validity of DSA and potential harms to the validity of DSA. A random sample of a second-hand Ebola tweet dataset was used to evaluate the validity of DSA compared to the manual coding approach and examine the influences of textual features on the validity of DSA. The results revealed substantial inconsistency between DSA and the manual coding approach. The presence of certain textual features such as negation can partially account for the inconsistency between DSA and manual coding. The findings imply that scholars should be careful and critical about findings in disease-related public health research that use DSA. Certain textual features should be more carefully addressed in DSA.
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Project description:BackgroundAlthough vaccination rates are above the threshold for herd immunity in South Korea, a growing number of parents have expressed concerns about the safety of vaccines. It is important to understand these concerns so that we can maintain high vaccination rates.ObjectiveThe aim of this study was to develop a childhood vaccination ontology to serve as a framework for collecting and analyzing social data on childhood vaccination and to use this ontology for identifying concerns about and sentiments toward childhood vaccination from social data.MethodsThe domain and scope of the ontology were determined by developing competency questions. We checked if existing ontologies and conceptual frameworks related to vaccination can be reused for the childhood vaccination ontology. Terms were collected from clinical practice guidelines, research papers, and posts on social media platforms. Class concepts were extracted from these terms. A class hierarchy was developed using a top-down approach. The ontology was evaluated in terms of description logics, face and content validity, and coverage. In total, 40,359 Korean posts on childhood vaccination were collected from 27 social media channels between January and December 2015. Vaccination issues were identified and classified using the second-level class concepts of the ontology. The sentiments were classified in 3 ways: positive, negative or neutral. Posts were analyzed using frequency, trend, logistic regression, and association rules.ResultsOur childhood vaccination ontology comprised 9 superclasses with 137 subclasses and 431 synonyms for class, attribute, and value concepts. Parent's health belief appeared in 53.21% (15,709/29,521) of posts and positive sentiments appeared in 64.08% (17,454/27,236) of posts. Trends in sentiments toward vaccination were affected by news about vaccinations. Posts with parents' health belief, vaccination availability, and vaccination policy were associated with positive sentiments, whereas posts with experience of vaccine adverse events were associated with negative sentiments.ConclusionsThe childhood vaccination ontology developed in this study was useful for collecting and analyzing social data on childhood vaccination. We expect that practitioners and researchers in the field of childhood vaccination could use our ontology to identify concerns about and sentiments toward childhood vaccination from social data.
| S-EPMC6592483 | biostudies-literature
Project description:Information gathering has become an integral part of assessing people's behaviors and actions. The Internet is used as an online learning site for sharing and exchanging ideas. People can actively give their reviews and recommendations for variety of products and services using popular social sites and personal blogs. Social networking sites, including Twitter, Facebook, and Google+, are examples of the sites used to share opinion. The stock market (SM) is an essential area of the economy and plays a significant role in trade and industry development. Predicting SM movements is a well-known and area of interest to researchers. Social networking perfectly reflects the public's views of current affairs. Financial news stories are thought to have an impact on the return of stock trend prices and many data mining techniques are used address fluctuations in the SM. Machine learning can provide a more accurate and robust approach to handle SM-related predictions. We sought to identify how movements in a company's stock prices correlate with the expressed opinions (sentiments) of the public about that company. We designed and implemented a stock price prediction accuracy tool considering public sentiment apart from other parameters. The proposed algorithm considers public sentiment, opinions, news and historical stock prices to forecast future stock prices. Our experiments were performed using machine-learning and deep-learning methods including Support Vector Machine, MNB classifier, linear regression, Naïve Bayes and Long Short-Term Memory. Our results validate the success of the proposed methodology.
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Project description:BackgroundEndometriosis is a debilitating and difficult-to-diagnose gynecological disease. Owing to limited information and awareness, women often rely on social media platforms as a support system to engage in discussions regarding their disease-related concerns.ObjectiveThis study aimed to apply computational techniques to social media posts to identify discussion topics about endometriosis and to identify themes that require more attention from health care professionals and researchers. We also aimed to explore whether, amid the challenging nature of the disease, there are themes within the endometriosis community that gather posts with positive sentiments.MethodsWe retrospectively extracted posts from the subreddits r/Endo and r/endometriosis from January 2011 to April 2022. We analyzed 45,693 Reddit posts using sentiment analysis and topic modeling-based methods in machine learning.ResultsSince 2011, the number of posts and comments has increased steadily. The posts were categorized into 11 categories, and the highest number of posts were related to either asking for information (Question); sharing the experiences (Rant/Vent); or diagnosing and treating endometriosis, especially surgery (Surgery related). Sentiment analysis revealed that 92.09% (42,077/45,693) of posts were associated with negative sentiments, only 2.3% (1053/45,693) expressed positive feelings, and there were no categories with more positive than negative posts. Topic modeling revealed 27 major topics, and the most popular topics were Surgery, Questions/Advice, Diagnosis, and Pain. The Survey/Research topic, which brought together most research-related posts, was the last in terms of posts.ConclusionsOur study shows that posts on social media platforms can provide insights into the concerns of women with endometriosis symptoms. The analysis of the posts confirmed that women with endometriosis have to face negative emotions and pain daily. The large number of posts related to asking questions shows that women do not receive sufficient information from physicians and need community support to cope with the disease. Health care professionals should pay more attention to the symptoms and diagnosis of endometriosis, discuss these topics with patients to reduce their dissatisfaction with doctors, and contribute more to the overall well-being of women with endometriosis. Researchers should also become more involved in social media and share new science-based knowledge regarding endometriosis.
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Project description:Individuals with autism spectrum disorder (ASD) demonstrate impairments with pragmatic (social) language, including narrative skills and conversational abilities. We aimed to quantitatively characterize narrative performance in ASD using natural language processing techniques: sentiment and language abstraction analyses based on the Linguistic Category Model. Individuals with ASD and with typical development matched for age, gender, ethnicity, and verbal and nonverbal intelligence quotients produced language samples during two standardized tasks from the Autism Diagnostic Observation Schedule, Second Edition assessment: Telling a Story from a Book and Description of a Picture. Only the narratives produced during the Book Task differed between ASD and control groups in terms of emotional polarity and language abstraction. Participants with typical development used words with positive sentiment more often in comparison to individuals with ASD. In the case of words with negative sentiment, the differences were marginally significant (participants with typical development used words with negative sentiment more often). The Book Task narratives of individuals with ASD were also characterized by a lower level of language abstraction than narratives of peers with typical development. Linguistic abstraction was strongly positively correlated with a higher number of words with emotional polarity. Neither linguistic abstraction nor emotional polarity correlated with participants' age or verbal and nonverbal IQ. The results support the promise of sentiment and language abstraction analyses as a useful tool for the quantitative, fully automated assessment of narrative abilities among individuals with ASD.
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Project description:To track online emotional expressions on social media platforms close to real-time during the COVID-19 pandemic, we built a self-updating monitor of emotion dynamics using digital traces from three different data sources in Austria. This allows decision makers and the interested public to assess dynamics of sentiment online during the pandemic. We used web scraping and API access to retrieve data from the news platform derstandard.at, Twitter, and a chat platform for students. We documented the technical details of our workflow to provide materials for other researchers interested in building a similar tool for different contexts. Automated text analysis allowed us to highlight changes of language use during COVID-19 in comparison to a neutral baseline. We used special word clouds to visualize that overall difference. Longitudinally, our time series showed spikes in anxiety that can be linked to several events and media reporting. Additionally, we found a marked decrease in anger. The changes lasted for remarkably long periods of time (up to 12 weeks). We have also discussed these and more patterns and connect them to the emergence of collective emotions. The interactive dashboard showcasing our data is available online at http://www.mpellert.at/covid19_monitor_austria/. Our work is part of a web archive of resources on COVID-19 collected by the Austrian National Library.
| S-EPMC7931924 | biostudies-literature