scholarly journals Sentimental Analysis and Detection of Rumours for Social Media Data using Logistic Regression

2019 ◽  
Vol 9 (1) ◽  
pp. 2123-2126
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Sentiment Analysis ◽  
User Generated Content ◽  
Ancient Text ◽  
Social Media Data ◽  
Social Media Platform ◽  
Subjective Knowledge ◽  
Media Platform ◽  
Media Data

Over the last decade ,the Internet has become an ubiquitous and enormous suffuse medium of the user generated content and self-opinionated knowledge. Users currently have the facility to specify their views, opinions and ideas publically. Victimizing social media platform is a place where people can express their mindsets and feelings in a well associated manner and hence is productive and economical . These ever-growing subjective knowledge are doubtless, an especially made for supply of data of any reasonably method process. The Sentiment Analysis aims at distinctive self-opinionated knowledge during an Internet and classifying them in line with their polarity whether or not they contain positive ,negative or neutralizing references. Sentiment Analysis could be a drawback of text based mostly analysis however there are difficulties which are needed to be pondered upon that would create a tough parameter as compared to ancient text based analysis. It depicts the state where it has a desire of trial to figure out these issues and it's spread out many chances for further analysis for handling negative sentences, hidden emotions , slangs and sentence sarcasm. The project also proposes additional features compared to other previous model projects by enabling the detection of rumor , identifying and analyzing whether message given via user belongs to rumor category or not using Logistic Regression process in Machine Learning domain.

scholarly journals Understanding Public Attitudes Toward Researchers Using Social Media for Detecting and Monitoring Adverse Events Data: Multi Methods Study

2019 ◽  
Vol 21 (8) ◽  
pp. e7081 ◽  
Author(s):  
Su Golder ◽  
Arabella Scantlebury ◽  
Helen Christmas
Keyword(s):  
Social Media ◽  
Informed Consent ◽  
Adverse Events ◽  
Potential Benefit ◽  
Influential Factor ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Social Media Platform ◽  
Media Platform ◽  
Media Data

Background Adverse events are underreported in research studies, particularly randomized controlled trials and pharmacovigilance studies. A method that researchers could use to identify more complete safety profiles for medications is to use social media analytics. However, patient’s perspectives on the ethical issues associated with using patient reports of adverse drug events on social media are unclear. Objective The objective of this study was to explore the ethics of using social media for detecting and monitoring adverse events for research purposes using a multi methods approach. Methods A multi methods design comprising qualitative semistructured interviews (n=24), a focus group (n=3), and 3 Web-based discussions (n=20) with members of the public was adopted. Findings from a recent systematic review on the use of social media for monitoring adverse events provided a theoretical framework to interpret the study’s findings. Results Views were ascertained regarding the potential benefits and harms of the research, privacy expectations, informed consent, and social media platform. Although the majority of participants were supportive of social media content being used for research on adverse events, a small number of participants strongly opposed the idea. The potential benefit of the research was cited as the most influential factor to whether participants would give their consent to their data being used for research. There were also some caveats to people’s support for the use of their social media data for research purposes: the type of social media platform and consideration of the vulnerability of the social media user. Informed consent was regarded as difficult to obtain and this divided the opinion on whether it should be sought. Conclusions Social media users were generally positive about their social media data being used for research purposes; particularly for research on adverse events. However, approval was dependent on the potential benefit of the research and that individuals are protected from harm. Further study is required to establish when consent is required for an individual’s social media data to be used.

Social Media as Mirror of Society

2017 ◽  
pp. 128-141
Author(s):  
Amir Manzoor
Keyword(s):  
Social Media ◽  
Application Programming Interface ◽  
Social Media Data ◽  
Social Media Platform ◽  
Application Programming ◽  
Media Platform ◽  
Use Of Social Media ◽  
Media Data ◽  
Programming Interface ◽  
Specific Reason

Over the last decade, social media use has gained much attention of scholarly researchers. One specific reason of this interest is the use of social media for communication; a trend that is gaining tremendous popularity. Every social media platform has developed its own set of application programming interface (API). Through these APIs, the data available on a particular social media platform can be accessed. However, the data available is limited and it is difficult to ascertain the possible conclusions that can be drawn about society on the basis of this data. This chapter explores the ways social researchers and scientists can use social media data to support their research and analysis.

scholarly journals Mapping the languages of Twitter in Finland:

2020 ◽  
Vol 121 (1) ◽  
pp. 12-44
Author(s):  
Tuomo Hiippala ◽  
Tuomas Väisänen ◽  
Tuuli Toivonen ◽  
Olle Järv
Keyword(s):  
Social Media ◽  
Urban Areas ◽  
High Volume ◽  
User Generated Content ◽  
Social Media Data ◽  
Fine Grained ◽  
Rural And Urban Areas ◽  
Rural And Urban ◽  
Media Platform ◽  
Media Data

Twitter is a popular social media platform for scholarly research, because the user-generated content on the platform can also include geographic and temporal information. We collect a corpus of 38 million Twitter messages with two million geographical coordinates to map the languages used across Finland at the level of regions and municipalities. To cope with the high volume of social media data, we use automatic language identification and place of residence detection. We estimate the linguistic richness and diversity of users and locations using measures developed within ecology and information sciences. The analyses reveal a rich, multilingual environment that varies geographically and temporally, particularly between coastal, rural and urban areas. The results, which underline the mutual benefits of collaboration between linguists and geographers, provide a more fine-grained, accurate and comprehensive view of the languages used on Twitter in Finland than previously available.

Comparison of different neural networks in sentiment analysis of social media data

2021 ◽  
Author(s):  
Vadim Moshkin ◽  
Andrew Konstantinov ◽  
Nadezhda Yarushkina ◽  
Alexander Dyrnochkin
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Social Media Data ◽  
Media Data

scholarly journals Sentiment Analysis in Social Media using Machine Learning Techniques

2020 ◽  
pp. 193-201 ◽  
Author(s):  
Hayder A. Alatabi ◽  
Ayad R. Abbas
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Great Success ◽  
Social Media Data ◽  
Learning Techniques ◽  
The World ◽  
Analysis System ◽  
Media Data

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

Product Review Analysis Using Social Media Data Based on Sentiment Analysis

2020 ◽  
pp. 527-533
Author(s):  
S. M. Mazharul Hoque Chowdhury ◽  
Sheikh Abujar ◽  
Ohidujjaman ◽  
Khalid Been Md. Badruzzaman ◽  
Syed Akhter Hossain
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Product Review ◽  
Social Media Data ◽  
Review Analysis ◽  
Media Data

Employing the Sentiment Analysis Tool in NVivo 11 Plus on Social Media Data

2017 ◽  
pp. 175-244
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Analysis Tool ◽  
Attitudes And Beliefs ◽  
Social Media Data ◽  
Public Issues ◽  
Public Events ◽  
Brand Reputation ◽  
Text Corpora ◽  
Media Data

Sentiment analysis has been used to assess people's feelings, attitudes, and beliefs, ranging from positive to negative, on a variety of phenomena. Several new autocoding features in NVivo 11 Plus enable the capturing of sentiment analysis and extraction of themes from text datasets. This chapter describes eight scenarios in which these tools may be applied to social media data, to (1) profile egos and entities, (2) analyze groups, (3) explore metadata for latent public conceptualizations, (4) examine trending public issues, (5) delve into public concepts, (6) observe public events, (7) analyze brand reputation, and (8) inspect text corpora for emergent insights.

scholarly journals Sentiment Analysis Methods for HPV Vaccines Related Tweets Based on Transfer Learning

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 307
Author(s):  
Li Zhang ◽  
Haimeng Fan ◽  
Chengxia Peng ◽  
Guozheng Rao ◽  
Qing Cong
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Hpv Vaccination ◽  
Experimental Results ◽  
Fine Tuning ◽  
Hpv Vaccines ◽  
Social Media Data ◽  
Public Sentiment ◽  
Media Data

The widespread use of social media provides a large amount of data for public sentiment analysis. Based on social media data, researchers can study public opinions on human papillomavirus (HPV) vaccines on social media using machine learning-based approaches that will help us understand the reasons behind the low vaccine coverage. However, social media data is usually unannotated, and data annotation is costly. The lack of an abundant annotated dataset limits the application of deep learning methods in effectively training models. To tackle this problem, we propose three transfer learning approaches to analyze the public sentiment on HPV vaccines on Twitter. One was transferring static embeddings and embeddings from language models (ELMo) and then processing by bidirectional gated recurrent unit with attention (BiGRU-Att), called DWE-BiGRU-Att. The others were fine-tuning pre-trained models with limited annotated data, called fine-tuning generative pre-training (GPT) and fine-tuning bidirectional encoder representations from transformers (BERT). The fine-tuned GPT model was built on the pre-trained generative pre-training (GPT) model. The fine-tuned BERT model was constructed with BERT model. The experimental results on the HPV dataset demonstrated the efficacy of the three methods in the sentiment analysis of the HPV vaccination task. The experimental results on the HPV dataset demonstrated the efficacy of the methods in the sentiment analysis of the HPV vaccination task. The fine-tuned BERT model outperforms all other methods. It can help to find strategies to improve vaccine uptake.

A Sentiment Analysis of the 2014-15 Ebola Outbreak in the Media and Social Media

2020 ◽  
pp. 202-212
Author(s):  
Blooma John ◽  
Bob Baulch ◽  
Nilmini Wickramasinghe
Keyword(s):  
Social Media ◽  
West Africa ◽  
Sentiment Analysis ◽  
Media Coverage ◽  
Social Media Platform ◽  
The Usa ◽  
Media Platform ◽  
The Media ◽  
Precautionary Measures ◽  
Increased Pressure

The negative and unbalanced nature of media and social media coverage has amplified anxieties and fears about the Ebola outbreak. The authors analyse news articles on the Ebola outbreak from two leading news outlets, together with comments on the articles from a well-known social media platform, from March 2014 to July 2015. The volume of news articles was greatest between August 2014 and January 2015, with a spike in October 2014, and was driven by the few cases of transmission in Europe and the USA. Sentiment analysis reveals coverage and commentary on the small number of Ebola cases in Europe and the USA were much more extensive than coverage and commentary on the outbreak in West Africa. Articles expressing negative sentiments were more common in the USA and also received more comments than those expressing positive sentiments. The negative sentiments expressed in the media and social media amplified fears about an Ebola outbreak outside West Africa, which increased pressure for unwarranted and wasteful precautionary measures.

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