Extraction of Named Entities from Social Media Text in Tamil Language Using N-Gram Embedding for Disaster Management

2019 ◽  
pp. 207-223
Author(s):  
G. Remmiya Devi ◽  
M. Anand Kumar ◽  
K. P. Soman
Keyword(s):  
Social Media ◽  
Disaster Management ◽  
Named Entities ◽  
Social Media Text ◽  
N Gram

Modeling Factuality Judgments in Social Media Text

2014 ◽  
Author(s):  
Sandeep Soni ◽  
Tanushree Mitra ◽  
Eric Gilbert ◽  
Jacob Eisenstein
Keyword(s):  
Social Media ◽  
Social Media Text

Bi-LSTM and Ensemble based Bilingual Sentiment Analysis for a Code-mixed Hindi-English Social Media Text

2020 ◽  
Author(s):  
Konark Yadav ◽  
Aashish Lamba ◽  
Dhruv Gupta ◽  
Ansh Gupta ◽  
Purnendu Karmakar ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Social Media Text

Improving sentiment scoring mechanism: a case study on airline services

2018 ◽  
Vol 118 (8) ◽  
pp. 1578-1596 ◽  
Author(s):  
Wandeep Kaur ◽  
Vimala Balakrishnan
Keyword(s):  
Social Media ◽  
Content Type ◽  
Exclamation Mark ◽  
Semantic Orientation ◽  
Social Media Text ◽  
Major Airlines ◽  
Using Data ◽  
Sentiment Score ◽  
Repeated Letter ◽  
Facebook Pages

Purpose The purpose of this paper is to investigate the effect of including letter repetition commonly found within social media text and its impact in determining the sentiment scores for two major airlines in Malaysia. Design/methodology/approach A Sentiment Intensity Calculator (SentI-Cal) was developed by assigning individual weights to each letter repetition, and tested it using data collected from official Facebook pages of the airlines. Findings Evaluation metrics indicate that SentI-Cal outperforms the baseline tool Semantic Orientation Calculator (SO-CAL), with an accuracy of 90.7 percent compared to 58.33 percent for SO-CAL. Practical implications A more accurate sentiment score allows airline services to easily obtain a better understanding of the sentiments of their customers, hence providing opportunities in improving their airline services. Originality/value Proposed mechanism calculates sentiment intensity of social media text by assigning individual weightage to each repeated letter and exclamation mark thus producing a more accurate sentiment score.

Communication Technology and Social Media: Opportunities and Implications for Healthcare Systems

2012 ◽  
Vol 17 (3) ◽  
Author(s):  
Betsy Weaver ◽  
Bill Lindsay ◽  
Betsy Gitelman
Keyword(s):  
Social Media ◽  
Communication Technology ◽  
Technology Implementation ◽  
Text Messaging ◽  
Current Evidence ◽  
Positive Outcomes ◽  
Social Media Text ◽  
E Mail ◽  
Improve Patient

Electronic patient education and communications, such as email, text messaging, and social media, are on the rise in healthcare today. This article explores potential uses of technology to seek solutions in healthcare for such challenges as modifying behaviors related to chronic conditions, improving efficiency, and decreasing costs. A brief discussion highlights the role of technologies in healthcare informatics and considers two theoretical bases for technology implementation. Discussion focuses more extensively on the ability and advantages of electronic communication technology, such as e-mail, social media, text messaging, and electronic health records, to enhance patient-provider e-communications in nursing today. Effectiveness of e-communication in healthcare is explored, including recent and emerging applications designed to improve patient–provider connections and review of current evidence supporting positive outcomes. The conclusion addresses the vision of nurses’ place in the vanguard of these developments.

Social media, diversity and vulnerability: their role in a disaster

2021 ◽  
Author(s):  
Olga Nardini ◽  
Sara Bonati ◽  
Stefano Morelli ◽  
Veronica Pazzi
Keyword(s):  
Social Media ◽  
Disaster Management ◽  
Cultural Context ◽  
Virtual Space ◽  
Management Process ◽  
Technological Support ◽  
Media Diversity ◽  
Disaster Studies ◽  
Relevant Point ◽  
Use Of Social Media

<p>Very few research studies have been dedicated to understanding the role of social media, diversity and vulnerability during a highly impacting event for a society. Social media are very important nowadays as a way to be in "connection to" and "link between" individuals. Thanks to technological support it is possible to create new virtual and real social relationships and networks and to be always up to date about what happen in the world. The role that virtual space plays "reducing distances", connecting people and places and facilitating the provision of support to people in need, has been receiving increasing interest in disaster studies in last years. In particular, connectivity has assumed an increasing role in relation to the diffusion of means to reach people and places in virtual mode. Furthermore, the use of social media as a means of providing information on disasters and risks could help to reduce exposure in disasters. However, several knowledge gaps are still opened, and in particular which are the potential repercussions of a high connected disaster management process on vulnerability? How can the weight of diversity change into the virtual space? The premise is that not everyone has the same possibility of accessing social media (e.g. to be informed, to know what is happening and to link with rescuers). The difficulty of accessing social media can make people invisible into the disaster management process with the risk that someone could be left behind. Thus, this presentation aims to discuss the challenges that derive from an increasing use of social platform in providing and receiving information during disasters. A second relevant point, that this presentation aims to discuss, is linked to the way citizens perceive communication platforms and how the flow of information significantly impacts on the interpretation and on the management of risk. Conclusions of this work suggest that communication should take into account the risk perception models by the public and therefore the peculiarities of each vulnerable group, to provide "targeted" communications in relation to the cultural context with the aim of reducing vulnerability growing up citizens’ awareness and knowledge. This presentation is the result of the work provided as part of the EU H2020 founded project LINKS (http://links-project.eu).<span> </span></p>

Spam Detection on Social Media Using Semantic Convolutional Neural Network

2018 ◽  
Vol 8 (1) ◽  
pp. 12-26 ◽  
Author(s):  
Gauri Jain ◽  
Manisha Sharma ◽  
Basant Agarwal
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Spam Detection ◽  
Learning Technology ◽  
The Social ◽  
Social Media Text ◽  
Current Article ◽  
Semantic Layer

This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.

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