scholarly journals Twitter Data Analysis using R

2019 ◽  
Vol 8 (4) ◽  
pp. 9159-9162
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Public Opinion ◽  
Data Analysis ◽  
Real Life ◽  
Unstructured Data ◽  
Video Images ◽  
Twitter Data ◽  
Wide Perspective ◽  
Twitter Data Analysis

Big Data is an emerging concept in the field of Data mining. It has numerous applications in real life. Most data are coming from social media networking Websites comprising of structured and unstructured data including Text, video, images etc. The main characteristics can be understood by five v’s. Twitter is one among the major evolving social media. Twitter Data analysis can be give you a wide perspective of public opinion regarding any product, public opinion etc which can be used to mine the knowledge from the data. For example, prediction analysis, product review, favourite among people tweets about GST (Goods and service tax).

scholarly journals Literature review on Real-time Location-Based Sentiment Analysis on Twitter

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Dilmini Rathnayaka ◽  
Pubudu K.P.N Jayasena ◽  
Iraj Ratnayake
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Sentiment Analysis ◽  
Real Time ◽  
Analysis Data ◽  
Time Data ◽  
Twitter Data ◽  
Analysis Methods ◽  
Real Time Data ◽  
Twitter Data Analysis

Sentiment analysis mainly supports sorting out the polarity and provides valuable information with the use of raw data in social media platforms. Many fields like health, business, and security require real-time data analysis for instant decision-making situations.Since Twitter is considered a popular social media platform to collect data easily, this paper is considering data analysis methods of Twitter data, real-time Twitter data analysis based on geo-location. Twitter data classification and analysis can be done with the use of diverse algorithms and deciding the most appropriate algorithm for data analysis, can be accomplished by implementing and testing these diverse algorithms.This paper is discussing the major description of sentiment analysis, data collection methods, data pre-processing, feature extraction, and sentiment analysis methods related to Twitter data. Real-time data analysis arises as a major method of analyzing the data available online and the real-time Twitter data analysis process is described throughout this paper. Several methods of classifying the polarized Twitter data are discussed within the paper while depicting a proposed method of Twitter data analyzing algorithm. Location-based Twitter data analysis is another crucial aspect of sentiment analyses, that enables data sorting according to geo-location, and this paper describes the way of analyzing Twitter data based on geo-location. Further, a comparison about several sentiment analysis algorithms used by previous researchers has been reported and finally, a conclusion has been provided.

scholarly journals Perception of social media behaviour among medical students, residents and medical specialists

2021 ◽  
Author(s):  
Sebastiaan A. Pronk ◽  
Simone L. Gorter ◽  
Scheltus J. van Luijk ◽  
Pieter C. Barnhoorn ◽  
Beer Binkhorst ◽  
...  
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Medical Students ◽  
Online Survey ◽  
Media Use ◽  
Demographic Data ◽  
Real Life ◽  
Clinical Work ◽  
Social Media Use ◽  
Medical Specialists

Abstract Introduction Behaviour is visible in real-life events, but also on social media. While some national medical organizations have published social media guidelines, the number of studies on professional social media use in medical education is limited. This study aims to explore social media use among medical students, residents and medical specialists. Methods An anonymous, online survey was sent to 3844 medical students at two Dutch medical schools, 828 residents and 426 medical specialists. Quantitative, descriptive data analysis regarding demographic data, yes/no questions and Likert scale questions were performed using SPSS. Qualitative data analysis was performed iteratively, independently by two researchers applying the principles of constant comparison, open and axial coding until consensus was reached. Results Overall response rate was 24.8%. Facebook was most popular among medical students and residents; LinkedIn was most popular among medical specialists. Personal pictures and/or information about themselves on social media that were perceived as unprofessional were reported by 31.3% of students, 19.7% of residents and 4.1% of medical specialists. Information and pictures related to alcohol abuse, partying, clinical work or of a sexually suggestive character were considered inappropriate. Addressing colleagues about their unprofessional posts was perceived to be mainly dependent on the nature and hierarchy of the interprofessional relation. Discussion There is a widespread perception that the presence of unprofessional information on social media among the participants and their colleagues is a common occurrence. Medical educators should create awareness of the risks of unprofessional (online) behaviour among healthcare professionals, as well as the necessity and ways of addressing colleagues in case of such lapses.

Data Mining Techniques and Applications

2018 ◽  
Vol 9 (1) ◽  
pp. 78-97 ◽  
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Unstructured Data ◽  
The Internet ◽  
Holistic View ◽  
Data Mining Techniques ◽  
Extant Literature ◽  
Business Environments ◽  
Data Volume ◽  
Complete Set

Data mining has been gaining attention with the complex business environments, as a rapid increase of data volume and the ubiquitous nature of data in this age of the internet and social media. Organizations are interested in making informed decisions with a complete set of data including structured and unstructured data that originate both internally and externally. Different data mining techniques have evolved over the last two decades. To solve a wide variety of business problems, different data mining techniques are developed. Practitioners and researchers in industry and academia continuously develop and experiment varieties of data mining techniques. This article provides an overview of data mining techniques that are widely used in different fields to discover knowledge and solve business problems. This article provides an update on data mining techniques based on extant literature as of 2018. That might help practitioners and researchers to have a holistic view of data mining techniques.

scholarly journals Mood State and Behavior Predictions in Social Media through Unstructured Data Analysis

2019 ◽  
pp. 1-9
Author(s):  
Gurpreet Singh Bawa ◽  
Suresh Kumar Sharma ◽  
Kanchan K. Jain
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Personality Trait ◽  
Mood State ◽  
Dirichlet Distribution ◽  
Probability Model ◽  
Unstructured Data ◽  
Latent Features ◽  
The Individual ◽  
And Behavior

For mood State and Behavior Predictions in Social Media through Unstructured Data Analysis, a new model, Behavior Dirichlet Probability Model (BDPM), which can capture the Behavior and Mood of user on Social media is proposed using Dirichlet distribution. There is a colossal amount of data being generated regularly on social media in the form of text from various channels by individuals in the form of posts, tweets, status, comments, blogs, reviews etc. Most of it belongs to some conversation where real-world individuals discuss, analyze, comment, exchange information. Deriving personality traits from textual data can be useful in observing the underlying attributes of the author’s personality which might explain a lot about their behavior, traits etc. These insights of the individual can be utilized to obtain a clear picture of their personality and accordingly a variety of services, utilities would follow automatically. Using Dirichlet probability distribution, the aim is to estimate the probability of each personality trait (or mood state) for an author and then model the latent features in the text which are not captured by the BDPM. As a result, the study can be helpful in prediction of mood state/personality trait as well as capturing the significance of the latent features apart from the ones present in the taxonomies, which will help in making an improved mood state or personality prediction.

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