A Review on Natural Disaster Detection in Social Media and Satellite Imagery Using Machine Learning and Deep Learning

2021 ◽  
Author(s):  
Swapandeep Kaur ◽  
Sheifali Gupta ◽  
Swati Singh ◽  
Tanvi Arora
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Natural Disaster ◽  
Satellite Imagery ◽  
Human Life ◽  
High Volume ◽  
Volume Data ◽  
The Social ◽  
Social Media Platforms

A disaster is a devastating incident that causes a serious disruption of the functions of a community. It leads to loss of human life and environmental and financial losses. Natural disasters cause damage and privation that could last for months and even years. Immediate steps need to be taken and social media platforms like Twitter help to provide relief to the affected public. However, it is difficult to analyze high-volume data obtained from social media posts. Therefore, the efficiency and accuracy of useful data extracted from the enormous posts related to disaster are low. Satellite imagery is gaining popularity because of its ability to cover large temporal and spatial areas. But, both the social media and satellite imagery require the use of automated methods to avoid the errors caused by humans. Deep learning and machine learning have become extremely popular for text and image classification tasks. In this paper, a review has been done on natural disaster detection through information obtained from social media and satellite images using deep learning and machine learning.

Deep Learning and Machine Learning Techniques for Analyzing Travelers' Online Reviews

2022 ◽  
pp. 20-39
Author(s):  
Elliot Mbunge ◽  
Benhildah Muchemwa
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Hospitality Industry ◽  
Learning Models ◽  
Online Data ◽  
Social Media Platforms ◽  
Using Data ◽  
Tourism And Hospitality Industry ◽  
Tourism And Hospitality

Social media platforms play a tremendous role in the tourism and hospitality industry. Social media platforms are increasingly becoming a source of information. The complexity and increasing size of tourists' online data make it difficult to extract meaningful insights using traditional models. Therefore, this scoping and comprehensive review aimed to analyze machine learning and deep learning models applied to model tourism data. The study revealed that deep learning and machine learning models are used for forecasting and predicting tourism demand using data from search query data, Google trends, and social media platforms. Also, the study revealed that data-driven models can assist managers and policymakers in mapping and segmenting tourism hotspots and attractions and predicting revenue that is likely to be generated, exploring targeting marketing, segmenting tourists based on their spending patterns, lifestyle, and age group. However, hybrid deep learning models such as inceptionV3, MobilenetsV3, and YOLOv4 are not yet explored in the tourism and hospitality industry.

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 Sentiment Analysis Of Twitter Data By Using Deep Learning And Machine Learning

2021 ◽  
Vol 12 (6) ◽  
pp. 962-970
Author(s):  
Prof. Manisha Sachin Dabade, Et. al.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Daily Monitoring ◽  
Fine Grain ◽  
Large Size ◽  
Twitter Data ◽  
The Social ◽  
Enormous Amount

In today’s world, social media is viral and easily accessible. The Social media sites like Twitter, Facebook, Tumblr, etc. are a primary and valuable source of information.Twitter is a micro-blogging platform, and it provides an enormous amount of data. Such type of information can use for different sentiment analysis applications such as reviews, predictions, elections, marketing, etc. It is one of the most popular sites where peoples write tweets, retweets, and interact daily. Monitoring and analyzing these tweets give valuable feedback to users. Due to this data's large size, sentiment analysis is using to analyze this data without going through millions of tweets manually. Any user writes their reviews about different products, topics, or events on Twitter, called tweets and retweets. People also use emojis such as happy, sad, and neutral in expressing their emotions, so these sites contain expansive volumes of unprocessed data called raw data. The main goal of this research is to recognize the algorithms by using Machine Learning Classifiers. The study intends to categorize Fine-grain sentiments within Tweets of Vaccination (89974 tweets) through machine learning and a deep learning approach. The study takes consideration of both labeled and unlabeled data. It also detects emojis from tweets using machine learning libraries like Textblob, Vadar, Fast text, Flair, Genism, spaCy, and NLTK.

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.

scholarly journals An Analysis of English Language Orthography, Typography and Grammar used by Millennial on Social Media Platforms

2021 ◽  
Vol 9 (2) ◽  
pp. 1-35
Author(s):  
Alia Khan ◽  
Prof. Mohammad Rizwan Khan
Keyword(s):  
Social Media ◽  
Education System ◽  
English Language ◽  
Human Life ◽  
Physical World ◽  
Negative Influence ◽  
Profound Influence ◽  
The People ◽  
The Social ◽  
Social Media Platforms

Social media is a term with which most of the people around the world are well acquainted. The advancement of technology has provided a new medium through which we can propose, deliver, swap, and share our ideas without moving a single inch. It is a new avenue for conveying information and a trend which is now-a-days in vogue. From infants to adults, everyone is somehow in contact with the social media. Similarly, education system too has a profound influence of social media. From placement institutes, school authority, teachers, learners, to parents in fact every stakeholder of education system is somehow tied to social media. Jeff Bezos, CEO at Amazon.com once described the power of social media by asserting that “If you make customers unhappy in the physical world, they might each tell 6 friends. If you make customers unhappy on the Internet, they can each tell 6,000 friends” (Pencak 2019). Thus, we can assume the potency and status of social media in our life. Though social media is affecting many significant areas of human life, but the area which itself is considered as a ‘systematic means of communication’ (that is ‘Language’) is too being swayed by this virtual medium. Social media has exceedingly affected English language skills. The paper explores how the social media has influenced linguistics habits of millennial, whether it has affected upcoming academicians in a positive or negative way, and what should be done in order to protect their linguistic habits from the negative influence of social media.

scholarly journals Combating Fake News, Misinformation, and Machine Learning Generated Fakes: Insight's from the Islamic Ethical Tradition

ICR Journal ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 189-212
Author(s):  
Talat Zubair ◽  
Amana Raquib ◽  
Junaid Qadir
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
World Wide ◽  
Security And Privacy ◽  
Fake News ◽  
Islamic Ethics ◽  
The World ◽  
Social Media Platforms ◽  
Filter Bubbles

The growing trend of sharing and acquiring news through social media platforms and the World Wide Web has impacted individuals as well as societies, spreading misinformation and disinformation. This trend—along with rapid developments in the field of machine learning, particularly with the emergence of techniques such as deep learning that can be used to generate data—has grave political, social, ethical, security, and privacy implications for society. This paper discusses the technologies that have led to the rise of problems such as fake news articles, filter bubbles, social media bots, and deep-fake videos, and their implications, while providing insights from the Islamic ethical tradition that can aid in mitigating them. We view these technologies and artifacts through the Islamic lens, concluding that they violate the commandment of spreading truth and countering falsehood. We present a set of guidelines, with reference to Qur‘anic and Prophetic teachings and the practices of the early Muslim scholars, on countering deception, putting forward ideas on developing these technologies while keeping Islamic ethics in perspective.

scholarly journals COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

2022 ◽  
Vol 9 ◽  
Author(s):  
Zunera Jalil ◽  
Ahmed Abbasi ◽  
Abdul Rehman Javed ◽  
Muhammad Badruddin Khan ◽  
Mozaherul Hoque Abul Hasanat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Daily Lives ◽  
The Public ◽  
Learning Techniques ◽  
Social Media Platforms ◽  
Twitter Users

The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.

scholarly journals A Review: Machine Learning Approach and Deep Learning Approach for Fake News Detection

2021 ◽  
Vol 9 (8) ◽  
pp. 1046-1050
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
New Technologies ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Fake News ◽  
Detection Techniques ◽  
Social Media Platforms ◽  
Extensive Growth

creasing number of social media platforms, emerging new technologies, and population growth which results in the rate of using social media has increased rapidly. With an increasing number of users on online platforms comes to a variety of problems like fake news. The extensive growth of fake news on social media can have a serious impact on the real world and became a cause of concern for net users and governments all over the world. Distinguishing between real news and fake news becoming more challenging. The amount of fake news has become a disguise. In this paper, we have done a survey on detection techniques for fake news using Algorithms and Deep learning techniques. We have compared machine learning algorithms like Naïve-Bayes, Decision tree, SVM, Adaboost, etc. Comparing the accuracy

scholarly journals Psychological Stress Prediction on Social Media using Convolutional Neural Network

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3464-3468
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Psychological Stress ◽  
Business Environment ◽  
Media Analysis ◽  
The Social ◽  
Stress Prediction

Psychological stress which is a mental illness also causes physical problems to the human. Nowadays social media plays an important role in the world for communication to share their thoughts with their friends and family. The social media analysis is the process of detecting and predicting the user's thoughts and opinions which also one of the important perspective in the developing business environment. The overwhelming stress and long term stress sometimes lead to suicidal ideation. By analyzing the social media content to predict the overwhelming stress state of the users in the earlier stage will reduce the psychological stress and suicidal rate too. In this paper, we address the problem of stress prediction by using social media. The machine learning and deep learning methods to perform the classification of stress analysis. Here both image and text- tweet data are used and the images are processed with the Optical Character Recognition and the text data are processed by using the Natural Language Processing and Convolutional Neural Network for classifying the tweet content of the user as stressed or non-stressed. Furthermore, with the advancement of the machine learning and deep learning method of classification gives a better result in terms of performance and accuracy of the prediction.

Representasi Feminisme dalam Video Klip ‘Nightmare’ oleh Halsey

Koneksi ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 338
Author(s):  
Faiz Zulia Maharany ◽  
Ahmad Junaidi
Keyword(s):  
Qualitative Research ◽  
Social Media ◽  
Data Collection ◽  
Mass Communication ◽  
Video Clip ◽  
Charles Sanders Peirce ◽  
Patriarchal Culture ◽  
The Social ◽  
Social Media Platforms ◽  
Barrier Wall

'Nightmare' is the title of a video clip belonging to a singer and singer called Halsey, in which the video clip is explained about the figure of women who struggle against patriarchal culture which has been a barrier wall for women to get their rights, welfare and the equality needed they get. This research uses descriptive qualitative research methods. Data collection techniques are done through documentation, observation and study of literature. Then, analyzed using Charles Sanders Peirce's semiotics technique. The results of this study show the fact that signs, symbols or messages representing feminism in the video, 'Nightmare' clips are presented through scenes that present women's actions in opposing domination over men and sarcastic sentences contained in the lyrics of the song to discuss with patriarchy. Youtube as one of the social media platforms where the 'Nightmare' video clip is uploaded is very effective for mass communication and for conveying the message contained in the video clip to the viewing public.‘Nightmare’ adalah judul video klip milik musisi sekaligus penyanyi yang bernama Halsey, dimana pada Video klipnya tersebut menceritakan tentang figur perempuan-perempuan yang berusaha melawan budaya patriarki yang selama ini telah menjadi dinding penghalang bagi perempuan untuk mendapatkan hak-haknya, keadilan dan kesetaraan yang seharusnya mereka dapatkan. Penelitian ini menggunakan metode penelitian kualitatif deskriptif. Teknik pengumpulan data dilakukan melalui dokumentasi, observasi dan studi kepustakaan. Kemudian, dianalisis menggunakan teknik semiotika milik Charles Sanders Peirce. Hasil penelitian ini menunjukan bahwa terdapat tanda-tanda, simbol atau pesan yang merepresentasikan feminisme di dalam video klip ‘Nightmare’ yang dihadirkan melalui adegan-adegan yang menyajikan aksi perempuan dalam menolak dominasi atas laki-laki dan kalimat-kalimat sarkas yang terkandung dalam lirik lagunya untuk ditujukan kepada patriarki. Youtube sebagai salah satu platform media sosial dimana video klip ‘Nightmare’ diunggah sangat efektif untuk melakukan komunikasi massa dan untuk menyampaikan pesan yang terkandung di dalam video klip tersebut kepada masyarakat yang menonton.

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