Disaster analysis through tweets (original) (raw)
Related papers
Informing Disasters Management by Using Twitter tweets
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Recently social media plays a major role and providing information during disasters. This paper mainly focuses on how people used social media, especially Twitter, in response to the country's worst flood, Earthquake that had occurred recently. And these tweets collecting analyzed using machine learning algorithms such as Naïve Bayes, Random Forests, Decision Tree, sentiment Analysis .during the disaster social media provides a surplus of information which includes information about the natural disaster, affected people's emotions, and relief efforts. And collect the tweets relating to disasters and build the sentimental classifier to categorize the user's emotions during disaster based on various distress levels. Various analysis techniques are applied in collecting tweets.
Visualizing Social Media Sentiment in Disaster Scenarios
Proceedings of the 24th International Conference on World Wide Web, 2015
Recently, social media, such as Twitter, has been successfully used as a proxy to gauge the impacts of disasters in real time. However, most previous analyses of social media during disaster response focus on the magnitude and location of social media discussion. In this work, we explore the impact that disasters have on the underlying sentiment of social media streams. During disasters, people may assume negative sentiments discussing lives lost and property damage, other people may assume encouraging responses to inspire and spread hope. Our goal is to explore the underlying trends in positive and negative sentiment with respect to disasters and geographically related sentiment. In this paper, we propose a novel visual analytics framework for sentiment visualization of geo-located Twitter data. The proposed framework consists of two components, sentiment modeling and geographic visualization. In particular, we provide an entropybased metric to model sentiment contained in social media data. The extracted sentiment is further integrated into a visualization framework to explore the uncertainty of public opinion. We explored Ebola Twitter dataset to show how visual analytics techniques and sentiment modeling can reveal interesting patterns in disaster scenarios.
Harnessing Sentiments towards Man-Made Disasters A Sentiment and Opinion Mining Analysis
— With the gargantuan amount of data being generated by various social media platforms, data scientists have come up with news ways to analyse and interpret data. These data are optimized by companies, institutions and establishments to create effective predictive models and analyse peoples' opinion and sentiments on a particular topic. Sentiment Analysis is the use of Natural Language Processing (NLP), statistics, or machine learning methods to extract, identify or otherwise characterize the sentiment of a text unit sometimes referred to as opinion mining [1]. Social media is now part of our daily lives. With its growing popularity it is now used by many as a platform where human opinions, thoughts, comments are expressed, exchanged and shared. The enormous amount of data that social media platforms generate are now being used by companies and government offices to create effective predictive models and analyse peoples' opinion and sentiments on a particular topic. Sentiment Analysis is the use of Natural Language Processing, statistics, or machine learning methods to extract, identify, or otherwise characterize the sentiment content of a text unit sometimes referred to as opinion mining [5]. Sentiment analysis has been widely used in a wide array of disciplines ranging from sociology, business, psychology, politics, education and disaster risk management to better comprehend the netizens' (social media users) sentiments over a particular topic and provide timely and appropriate responses. This paper will focus on the sentiment analysis of one of the most talked about and polarizing topics globally, man-made disasters. Made-made disaster is defined by Disaster Survival Resources [3] as disasters with element of human intent or negligence that leads to human suffering and environmental damage; many mirror natural disasters, yet man has a direct hand in their occurrence. This research is very timely, because of the so many geopolitical conflicts that are arising globally. This research focused on harnessing netizen tweets (from twitter, one of the largest social media platforms) related to man-made disasters. Each tweet will be analysed using a free and open source machine learning tool and finally determine the sentiment of each tweet. II. OBJECTIVES This study aimed to harness twitter data using a free and open-source machine learning tool and determine the overall sentiment of the collected tweets related to man-made disasters. III. CONCEPTUAL FRAMEWORK The conceptual framework of this study was based on the Knowledge Discovery in Database (KDD) Theory. KDD is focused on the development and application of various techniques for generating knowledge from enormous collection data called datasets [2]. One of the most common KDD technique is the application of data mining (DM) and machine learning tools. In figure 1, the social media giant Twitter, is an online repository of opinions, sentiments and comments of netizens in various topics. Using the twitter API and tweepy python module, tweets will be harnessed, captured and imported to Orange machine learning tool. Once the desired amount of tweets have been captured it will be processed and analyzed and finally generate the sentiment analysis results using the Text Mining function of Orange.
SENTIMENT TRENDS ON NATURAL DISASTERS USING LOCATION BASED TWITTER OPINION MINING
Analysis of public opinion from social media might yield attention results and insights into the planet of public opinions concerning natural disasters, service or temperament. Social network information is one in every of the foremost effective and correct indi mentioned a technique that permits utilization and interpretation of twitter information to see public opinions and trends on sentiment. Analysis was done on tweets concerning the natural disasters and female specific analysis has been enclosed. Sentiment trends are found on multiple components at different levels on people opinions, which were found however general consistency with outside news and statistics was a
Extracting Valuable Information from Twitter during Natural Disasters
Social media is a vital source of information during any major event, especially natural disasters. However, with the exponential increase in volume of social media data, so comes the increase in conversational data that does not provide valuable information, especially in the context of disaster events, thus, diminishing peoples' ability to find the information that they need in order to organize relief efforts, find help, and potentially save lives. This project focuses on the development of a Bayesian approach to the classification of tweets (posts on Twitter) during Hurricane Sandy in order to distinguish "informational" from "conversational" tweets. We designed an effective set of features and used them as input to Naïve Bayes classifiers. In comparison to a "bag of words" approach, the new feature set provides similar results in the classification of tweets. However, the designed feature set contains only 9 features compared with more than 3000 features for "bag of words." When the feature set is combined with "bag of words", accuracy achieves 85.2914%. If integrated into disaster-related systems, our approach can serve as a boon to any person or organization seeking to extract useful information in the midst of a natural disaster.
2022
Worldwide disasters like bushfires, earthquakes, floods, cyclones, heatwaves etc. have affected the lives of social media users in an unprecedented manner. They are constantly posting their level of negativity over the disaster situations at their location of interest. Understanding location-oriented sentiments about disaster situation is of prime importance for political leaders, and strategic decision-makers. To this end, we present a new fully automated algorithm based on artificial intelligence (AI) and Natural Language Processing (NLP), for extraction of location-oriented public sentiments on global disaster situation. We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to disaster in 110 languages through AI and NLP based sentiment analysis, named entity recognition (NER), anomaly detection, regression, and Getis Ord Gi* algorithms. We deployed and tested this algorithm on live Twitter feeds from 28 September 2021 till 6 Oc...
Twitter speaks: A case of national disaster situational awareness
Journal of Information Science
In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental and human losses. The unpredictable nature of natural disasters behaviour makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys is a traditional approach to analyse public concerns during natural disasters; however, this approach is limited, expensive and time-consuming. Luckily, the advent of social media has provided scholars with an alternative means of analysing public concerns. Social media enable users (people) to freely communicate their opinions and disperse information regarding current events including natural disasters. This research emphasises the value of social media analysis and proposes an analytical framework: Twitter Situational Awareness (TwiSA). This framework uses text mining methods including sentiment analysis and topic modelling to create a better SA for disaster prepare...
Sentiment Analysis of German Social Media Data for Natural Disasters
Analysis of social media and traditional media provides significant information to first responders in times of natural disasters. Sentiment analysis , particularly of social media originating from the affected population, forms an integral part of multifaceted media analysis. The current paper extends an existing methodology to the domain of natural disasters, broadens the support of multiple languages and introduces a new man ner of classification. The performance of the approach is evaluated on a recently collected dataset manually annotated by three human annotators as a reference. The experiments show a high agreement rate between the approach taken and the annotators. Furthermore, the paper presents the initial application of the resulting technology and models to sentiment analysis of social media data in German, covering data collected during the Central European floods of 2013.
The Analysis of Tweets to Detect Natural Hazards
2018
During times of disasters, users can act as powerful social sensors, because of the significant amount of data they generate on social media. Indeed, they contribute to creating situational awareness by informing what is happening in the affected community during the incident. In this context, this article focuses on the text-processing module in CASPER, a knowledge-based system that integrates event detection and sentiment tracking. The performance of the system was tested with the natural disaster of wildfires.