Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

Fake News Polarization for Sentiment Analysis

2022 ◽  
pp. 255-263
Author(s):  
Chirag Visani ◽  
Vishal Sorathiya ◽  
Sunil Lavadiya
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Opinion Mining ◽  
Easy Access ◽  
The Internet ◽  
Fake News ◽  
Social Media Networks ◽  
Sentiment Mining

The popularity of the internet has increased the use of e-commerce websites and news channels. Fake news has been around for many years, and with the arrival of social media and modern-day news at its peak, easy access to e-platform and exponential growth of the knowledge available on social media networks has made it intricate to differentiate between right and wrong information, which has caused large effects on the offline society already. A crucial goal in improving the trustworthiness of data in online social networks is to spot fake news so the detection of spam news becomes important. For sentiment mining, the authors specialise in leveraging Facebook, Twitter, and Whatsapp, the most prominent microblogging platforms. They illustrate how to assemble a corpus automatically for sentiment analysis and opinion mining. They create a sentiment classifier using the corpus that can classify between fake, real, and neutral opinions in a document.

scholarly journals Reviewing Sentiment Analysis at the Shallow End

2020 ◽  
Vol 8 (4) ◽  
pp. 47-62
Author(s):  
Francisca Oladipo ◽  
Ogunsanya, F. B ◽  
Musa, A. E. ◽  
Ogbuju, E. E ◽  
Ariwa, E.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Information Exchange ◽  
Training Data ◽  
Data Set ◽  
The Social ◽  
Machine Learning Approach ◽  
Media Space ◽  
Social Media Platforms

The social media space has evolved into a large labyrinth of information exchange platform and due to the growth in the adoption of different social media platforms, there has been an increasing wave of interests in sentiment analysis as a paradigm for the mining and analysis of users’ opinions and sentiments based on their posts. In this paper, we present a review of contextual sentiment analysis on social media entries with a specific focus on Twitter. The sentimental analysis consists of two broad approaches which are machine learning which uses classification techniques to classify text and is further categorized into supervised learning and unsupervised learning; and the lexicon-based approach which uses a dictionary without using any test or training data set, unlike the machine learning approach.  

scholarly journals Twitter Sentiment Analysis Model

2021 ◽  
Vol 9 (VII) ◽  
pp. 296-302
Author(s):  
Pushkar Dubey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Approach ◽  
Analysis Model ◽  
Market Place ◽  
The Social

Social networks are the main resources to gather information about people’s opinion towards different topics as they spend hours daily on social media and share their opinion. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers’ perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers’ perceptions. .We use natural language processing and machine learning concepts to create a model for analysis . In this paper we are discussing how we can create a model for analysis of twittes which is trained by various nlp , machine learning and Deep learning Approach.

Annotating and Detecting Topics from Social Media Forum and Modelling the Annotation to Derive Directions-A Case Study (Preprint)

2020 ◽  
Author(s):  
Athira B ◽  
Josette Jones ◽  
Sumam Mary Idicula ◽  
Anand Kulanthaivel ◽  
Sunandan Chakraborty ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Social Media ◽  
Semantic Annotation ◽  
Current Trend ◽  
Learning Model ◽  
Healthcare Sector ◽  
Final Decision ◽  
Breast Cancer Patients ◽  
Machine Learning Model

BACKGROUND Widespread influence on social media has its ramifications on all walks of life over the last few decades. Interestingly enough, the healthcare sector is a significant beneficiary of the reports and pronouncements that appear on social media. Although medics and other health professionals are the final decision-makers, advice or recommendations from kindred patients has consequential role. In full appreciation of the current trend, the present paper explores the topics pertaining to the patients, diagnosed with breast cancer as well as the survivors, who are discussing on online fora. OBJECTIVE The study examines the online forum of Breast Cancer.org (BCO), automatically maps discussion entries to formal topics, and proposes a machine learning model to characterize the topics in the health-related discussion, so as to elicit meaningful deliberations. Therefore, the study of communication messages draws conclusions about what matters to the patients. METHODS Manual annotation was made in the posts of a few randomly selected forums. To explore the topics of breast cancer patients and survivors, 736 posts are selected for semantic annotation. The entire process was automated using machine learning model falling into category of supervised learning algorithms. The effectiveness of those algorithms used for above process has been compared. RESULTS The method could classify following 8-high level topics, such as writing medication reviews, explaining the adverse effects of medication, clinician knowledge, various treatment options, seeking and supporting various matters, diagnostic procedures, financial issues and implications in everyday life. The model viz. Ensembled Neural Network (ENN) achieved a promising predicted score of 83.4 % F1-score among four different models. CONCLUSIONS The research was able to segregate and name the posts all into a set of 8 classes and supported by the efficient scheme for encoding text to vectors, the current machine learning models are shown to give impressive performance in modelling the annotation process.

Developing Machine Learning Model for Predicting Social Media Induced Fake News

2021 ◽  
pp. 656-669
Author(s):  
David Langley ◽  
Caoimhe Reidy ◽  
Mark Towey ◽  
Manisha ◽  
Denis Dennehy
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Learning Model ◽  
Fake News ◽  
Machine Learning Model

scholarly journals Sentiment analysis of Social Media Text-Emoticon Post with Machine learning Models Contribution Title

2021 ◽  
Vol 2070 (1) ◽  
pp. 012079
Author(s):  
V Jagadishwari ◽  
A Indulekha ◽  
Kiran Raghu ◽  
P Harshini
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Data Sets ◽  
Learning Models ◽  
Twitter Data ◽  
The Social ◽  
Social Media Text ◽  
Machine Learning Models

Abstract Social Media is an arena in recent times for people to share their perspectives on a variety of topics. Most of the social interactions are through the Social Media. Though all the Online Social Networks allow users to express their views and opinions in many forms like audio, video, text etc, the most popular form of expression is text, Emoticons and Emojis. The work presented in this paper aims at detecting the sentiments expressed in the Social Media posts. The Machine Learning Models namely Bernoulli Bayes, Multinomial Bayes, Regression and SVM were implemented. All these models were trained and tested with Twitter Data sets. Users on Twitter express their opinions in the form of tweets with limited characters. Tweets also contain Emoticons and Emojis therefore Twitter data sets are best suited for the sentiment analysis. The effect of emoticons present in the tweet is also analyzed. The models are first trained only with the text and then they are trained with text and emoticon in the tweet. The performance of all the four models in both cases are tested and the results are presented in the paper.

scholarly journals NOT EVEN THE SKY IS THE LIMIT: THE MEANINGS OF CONSUMPTION AND THE DYNAMICS OF SOCIAL MOBILITY ON THE @blogueiradebaixarenda PROFILE ON INSTAGRAM AND YOUTUBE

2020 ◽  
Vol 10 (3) ◽  
pp. 831-859
Author(s):  
Carla Barros
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Mobility ◽  
Low Income ◽  
Online Social Networks ◽  
Social Dynamics ◽  
Observational Research ◽  
Cultural Mediator ◽  
Consumption Practices ◽  
Social Media Platforms

Abstract The article sets out to explore the meanings surrounding consumption on the @blogueiradebaixarenda profile on the Instagram and YouTube online social networks, considering the perceptions of materiality and their articulations with the dynamics of social mobility. It analyses the elements making up the “low-income lifestyle” as a native category within the context of “digital influencers.” Through online observational research, the posts, hashtags and comments on both social media platforms were analysed, seeking to explore how consumption practices appear as mediators of social dynamics and identity constructs. Among the results, the articulations between materiality and social mobility, the idea of minimalism within the “low-income lifestyle” and the blogger’s status as a cultural mediator are highlighted.

Sign in / Sign up

Export Citation Format

Share Document