scholarly journals Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

2021 ◽  
Vol 11 (22) ◽  
pp. 10694
Author(s):  
Nora Alturayeif ◽  
Hamzah Luqman
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Loss Function ◽  
Psychological Effects ◽  
Fine Grained ◽  
Current Events ◽  
Social Media Platforms ◽  
Sentiment Detection ◽  
Textual Representation ◽  
Almost All

The outbreak of coronavirus disease (COVID-19) has affected almost all of the countries of the world, and has had significant social and psychological effects on the population. Nowadays, social media platforms are being used for emotional self-expression towards current events, including the COVID-19 pandemic. The study of people’s emotions in social media is vital to understand the effect of this pandemic on mental health, in order to protect societies. This work aims to investigate to what extent deep learning models can assist in understanding society’s attitude in social media toward COVID-19 pandemic. We employ two transformer-based models for fine-grained sentiment detection of Arabic tweets, considering that more than one emotion can co-exist in the same tweet. We also show how the textual representation of emojis can boost the performance of sentiment analysis. In addition, we propose a dynamically weighted loss function (DWLF) to handle the issue of imbalanced datasets. The proposed approach has been evaluated on two datasets and the attained results demonstrate that the proposed BERT-based models with emojis replacement and DWLF technique can improve the sentiment detection of multi-dialect Arabic tweets with an F1-Micro score of 0.72.

Review of Sentiment Detection

2019 ◽  
Vol 11 (1) ◽  
pp. 44-53
Author(s):  
Smiley Gupta ◽  
Jagtar Singh
Keyword(s):  
Social Media ◽  
Comparative Analysis ◽  
Sentiment Analysis ◽  
Large Volume ◽  
User Generated Content ◽  
Automated System ◽  
Social Media Platforms ◽  
Sentiment Detection

A large volume of user-generated data is evolving on a day-to-day basis, especially on social media platforms like Twitter, where people express their opinions and emotions regarding certain individuals or entities. This user-generated content becomes very difficult to analyze manually and therefore requires a need for an intelligent automated system which mines the opinions and organizes them using polarity metrics, representing the process of sentiment analysis. The motive of this review is to study the concept of sentiment analysis and discuss the comparative analysis of its techniques along with the challenges in this field to be considered for future enhancement.

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.

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 AthPPA: A Data Visualization Tool for Identifying Political Popularity over Twitter

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 312
Author(s):  
Alexandros Britzolakis ◽  
Haridimos Kondylakis ◽  
Nikolaos Papadakis
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Real Time ◽  
Data Visualization ◽  
Visualization Tool ◽  
Political Leaders ◽  
Political Event ◽  
Data Analyst ◽  
Industrial Sectors ◽  
Social Media Platforms

Sentiment Analysis is an actively growing field with demand in both scientific and industrial sectors. Political sentiment analysis is used when a data analyst wants to determine the opinion of different users on social media platforms regarding a politician or a political event. This paper presents Athena Political Popularity Analysis (AthPPA), a tool for identifying political popularity over Twitter. AthPPA is able to collect in-real-time tweets and for each tweet to extract metadata such as number of likes, retweets per tweet etc. Then it processes their text in order to calculate their overall sentiment. For the calculation of sentiment analysis, we have implemented a sentiment analyzer that is able to identify the grammatical issues of a sentence as well as a lexicon of negative and positive words designed specifically for political sentiment analysis. An analytic engine processes the collected data and provides different visualizations that provide additional insights on the collected data. We show how we applied our framework to the three most prominent Greek political leaders in Greece and present our findings there.

scholarly journals Fine-grained sentiment analysis of social media with emotion sensing

2016 ◽  
Author(s):  
Zhaoxia Wang ◽  
Chee Seng Chong ◽  
Landy Lan ◽  
Yinping Yang ◽  
Seng Beng Ho ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Fine Grained

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 Electronic Cigarette Users' Perspective on the COVID-19 Pandemic: Observational Study Using Twitter Data (Preprint)

2020 ◽  
Author(s):  
Yankun Gao ◽  
Zidian Xie ◽  
Dongmei Li
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Viral Infections ◽  
Application Programming Interface ◽  
Electronic Cigarette ◽  
Twitter Data ◽  
Social Media Platforms ◽  
Twitter Users ◽  
Public Health Practitioners

BACKGROUND Previous studies have shown that electronic cigarette (e-cigarette) users might be more vulnerable to COVID-19 infection and could develop more severe symptoms if they contract the disease owing to their impaired immune responses to viral infections. Social media platforms such as Twitter have been widely used by individuals worldwide to express their responses to the current COVID-19 pandemic. OBJECTIVE In this study, we aimed to examine the longitudinal changes in the attitudes of Twitter users who used e-cigarettes toward the COVID-19 pandemic, as well as compare differences in attitudes between e-cigarette users and nonusers based on Twitter data. METHODS The study dataset containing COVID-19–related Twitter posts (tweets) posted between March 5 and April 3, 2020, was collected using a Twitter streaming application programming interface with COVID-19–related keywords. Twitter users were classified into two groups: Ecig group, including users who did not have commercial accounts but posted e-cigarette–related tweets between May 2019 and August 2019, and non-Ecig group, including users who did not post any e-cigarette–related tweets. Sentiment analysis was performed to compare sentiment scores towards the COVID-19 pandemic between both groups and determine whether the sentiment expressed was positive, negative, or neutral. Topic modeling was performed to compare the main topics discussed between the groups. RESULTS The US COVID-19 dataset consisted of 4,500,248 COVID-19–related tweets collected from 187,399 unique Twitter users in the Ecig group and 11,479,773 COVID-19–related tweets collected from 2,511,659 unique Twitter users in the non-Ecig group. Sentiment analysis showed that Ecig group users had more negative sentiment scores than non-Ecig group users. Results from topic modeling indicated that Ecig group users had more concerns about deaths due to COVID-19, whereas non-Ecig group users cared more about the government’s responses to the COVID-19 pandemic. CONCLUSIONS Our findings show that Twitter users who tweeted about e-cigarettes had more concerns about the COVID-19 pandemic. These findings can inform public health practitioners to use social media platforms such as Twitter for timely monitoring of public responses to the COVID-19 pandemic and educating and encouraging current e-cigarette users to quit vaping to minimize the risks associated with COVID-19.

scholarly journals Sentiment Analysis of Social Media Content for Music Recommendation

2021 ◽  
Vol 4 (1) ◽  
pp. 110-120
Author(s):  
S Akuma ◽  
P Obilikwu ◽  
E Ahar
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Recommender System ◽  
Emotional State ◽  
Media Content ◽  
Random Testing ◽  
Music Recommendation ◽  
Social Media Platforms ◽  
High Predictability ◽  
Use Of Social Media

There is a growing use of social media for communication and entertainment. The information obtained from these social media platforms like Facebook, Linkedln, Twitter and so on can be used for inferring users’ emotional state. Users express their emotions on social media such as Twitter through text and emojis. Such expression can be harvested for the development of a recommender system. In this work, live tweets of users were harvested for the development of an emotion-based music recommender system. The emotions captured in this work include happy, fear, angry disgusted and sad. Users tweets in the form of emojis or text were matched with predefined variables to predict the emotion of users. Random testing of live tweets using the system was conducted and the result showed high predictability.

scholarly journals Ternary Classification of Product Based Reviews: Survey, Open Issues and New Approach for Sentiment Analysis

2021 ◽  
Vol 1 (2) ◽  
pp. 1-8
Author(s):  
Sushila Sonare ◽  
Megha Kamble
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Text Data ◽  
New Approach ◽  
Meaningful Information ◽  
Multi Class Classification ◽  
Almost All ◽  
Open Issues

Now-a-days, it is very common that the customers share their thoughts about any product, brand and their experience in social media. The analysts collect these reviews and process it, to extract meaningful information about the product. The beauty of social media is, it’s involved in all the domains. So the analysts got reviews from different social media and platforms for almost all kind of thing. The Sentiment Analysis is applied to predict outcomes for getting useful information, for ex.; like predict the blockbuster for a movie, rating for any new launches and many more. This type of prediction is really helpful for the customer to buy any goods or take any services in this competitive world. This paper is focused on e-commerce website reviews which are normally in text form with some special characters and some symbols (emojis). Each word in this text set got some meaning in terms of context, emotion and prior experience. These characteristics contribute to some of the features of text data for prediction. The objective of this paper is to compile existing research works on text analysis and emotion based analysis. The open issues and challenges of document based sentiment analysis are also discussed. The paper concluded with proposing a new approach of multi class classification. Ternary classification for classes positive, negative and neutral is suggested primarily for product based text and emoji reviews on Twitter social media.

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