Detecting New Events from Microblogs Using Convolutional Neural Networks
01 Jan 2020-pp 1-9
TL;DR: In this paper, a convolution neural network (CNN) technique is used to identify the retrospective event from the microblog and the proposed CNN shows the event detection high accuracy which in turn yields improved performance when compared with other state-of-the-art methods.
Abstract: Online social networks turn out to be a potential data source to discover worthwhile information from microblogs. Conversely, time-critical exploration of microblog data during catastrophic events such as flood, cyclone, forest fire, and violence carries critical challenges to machine learning techniques. For instance, the microblog from Twitter is utilized to identify event along with its location-specific orientation. In this paper, convolution neural network (CNN) technique is used to identify the retrospective event from the microblog. The existing state of-the-art classification methodologies require substantial volume of labeled data detailed to an unambiguous event during training phase. In addition, it requires feature to attain better outcomes. During the experiments, the n-gram CNN model is trained from the tweets intended for multi-class tweet classification which was related to the specified events in the past without feature engineering. The proposed CNN shows the event detection high accuracy which in turn yields improved performance when compared with other state-of-the-art methods.
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TL;DR: In this article , the authors proposed a framework focusing on the recognizable proof of fundamental enthusiastic states like indignation, happiness, nonpartisan, and pity from human voice tests, highlights like MEL frequency cepstral coefficient and energy are utilized.
Abstract: Emotion Recognition through voice tests is a new exploration subject in the Human-Computer Interaction (HCI) field. The requirement for it has emerged for an all the more simple correspondence interface among people and PCs since PCs have become the fundamental piece of our lives. To accomplish this objective, a PC would need to have the option to separate its present circumstance and react contrastingly relying upon that specific perception. The proposed human identification includes understanding a client’s passionate state and to make the human-PC cooperation more regular, the principle objective is that the PC ought to have the option to perceive the enthusiastic conditions of people in equivalent to a human does. The proposed framework focuses on the recognizable proof of fundamental enthusiastic states like indignation, happiness, nonpartisan, and pity from human voice tests. While characterizing various speech recognitions, highlights like MEL frequency cepstral coefficient and energy are utilized. The proposed strategy depicts and thinks about the exhibitions of learning multiclass Support Vector Machine (SVM) , Random Forest (RF) and their mix of speech recognition acknowledgment. The MFCC and SVM algorithm proves to be an efficient no-regret online algorithm which detects the speech recognition with average classification accuracy of 89% which is reasonably acceptable.
TL;DR: In this article , a fusion of Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) models are applied on non-probability samplings of Twitter data collected during lockdown situations to detect the depressiveness condition.
Abstract: Online social media provide benign choices for online users to discuss psychological issues like depression which they prefer to share on Twitter, Facebook platforms. In specific, during lockdown situations due to Covid-19, most of the people isolated from societal interaction and left untreated might lead to uncertain mental conditions. Due to the stigma attached to mental illness, many people undergo a depressive state and vent it out on social media. In this paper, a fusion of Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) models are applied on non-probability samplings of Twitter data collected during lockdown situations to detect the depressiveness condition. The dynamic chatbot is developed using Natural Language Processing (NLP) to recover similar depressive online users. Moreover, the experiments demonstrate the fusing model selector chooses the deep learning techniques to predict the user behavior with high accuracy.KeywordsSocial networksTwitterUser similarityMental healthNeural NetworksChatbot
TL;DR: In this paper , a real-time disaster mining from twitter disaster event mining using apache spark for natural disasters is proposed by fusing text, images and geo-spatial data.
References
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26 Apr 2010
TL;DR: This paper investigates the real-time interaction of events such as earthquakes in Twitter and proposes an algorithm to monitor tweets and to detect a target event and produces a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location.
Abstract: Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (96% of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA.
3,976 citations
TL;DR: An earthquake reporting system for use in Japan is developed and an algorithm to monitor tweets and to detect a target event is proposed, which produces a probabilistic spatiotemporal model for the target event that can find the center of the event location.
Abstract: Twitter has received much attention recently. An important characteristic of Twitter is its real-time nature. We investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center of the event location. We regard each Twitter user as a sensor and apply particle filtering, which are widely used for location estimation. The particle filter works better than other comparable methods for estimating the locations of target events. As an application, we develop an earthquake reporting system for use in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (93 percent of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and notification is delivered much faster than JMA broadcast announcements.
483 citations
TL;DR: A real-time monitoring system for traffic event detection from Twitter stream analysis that fetches tweets from Twitter according to several search criteria; processes tweets, by applying text mining techniques; and finally performs the classification of tweets.
Abstract: Social networks have been recently employed as a source of information for event detection, with particular reference to road traffic congestion and car accidents. In this paper, we present a real-time monitoring system for traffic event detection from Twitter stream analysis. The system fetches tweets from Twitter according to several search criteria; processes tweets, by applying text mining techniques; and finally performs the classification of tweets. The aim is to assign the appropriate class label to each tweet, as related to a traffic event or not. The traffic detection system was employed for real-time monitoring of several areas of the Italian road network, allowing for detection of traffic events almost in real time, often before online traffic news web sites. We employed the support vector machine as a classification model, and we achieved an accuracy value of 95.75% by solving a binary classification problem (traffic versus nontraffic tweets). We were also able to discriminate if traffic is caused by an external event or not, by solving a multiclass classification problem and obtaining an accuracy value of 88.89%.
303 citations
26 Oct 2010
TL;DR: This paper addresses the task of identifying controversial events using Twitter as a starting point: it proposes 3 models for this task and reports encouraging initial results.
Abstract: Social media provides researchers with continuously updated information about developments of interest to large audiences. This paper addresses the task of identifying controversial events using Twitter as a starting point: we propose 3 models for this task and report encouraging initial results.
246 citations
Proceedings Article•
19 Jun 2011TL;DR: A graphical model is developed that addresses record extraction from social streams such as Twitter by learning a latent set of records and a record-message alignment simultaneously, resulting in a set of canonical records that are consistent with aligned messages.
Abstract: We present a novel method for record extraction from social streams such as Twitter. Unlike typical extraction setups, these environments are characterized by short, one sentence messages with heavily colloquial speech. To further complicate matters, individual messages may not express the full relation to be uncovered, as is often assumed in extraction tasks. We develop a graphical model that addresses these problems by learning a latent set of records and a record-message alignment simultaneously; the output of our model is a set of canonical records, the values of which are consistent with aligned messages. We demonstrate that our approach is able to accurately induce event records from Twitter messages, evaluated against events from a local city guide. Our method achieves significant error reduction over baseline methods.
214 citations