Sentiment analysis during Hurricane Sandy in emergency response

Neppalli, Venkata Kishore; Caragea, Cornelia; Squicciarini, Anna; Tapia, Andrea H.; Stehle, Sam

doi:10.1016/j.ijdrr.2016.12.011

Cited by 164 publications

(92 citation statements)

References 18 publications

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“…geolocation, and stakeholders power) in a small sample of tweets to find patterns of information dissemination [42]. Recently, a study mapped online users' sentiments to track their changes during a hurricane [11]. Twitter data was also used to help disaster management using qualitative content analysis to categorize information inside the Twitter data during a volcano eruption [43].…”

Section: Related Workmentioning

confidence: 99%

Twitter speaks: A case of national disaster situational awareness

Karami

Shah

Vaezi

et al. 2019

Journal of Information Science

116

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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 behavior makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys is a traditional approach to analyze 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 analyzing public concerns. Social media enable users (people) to freely communicate their opinions and disperse information regarding current events including natural disasters. This research emphasizes 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 modeling to create a better SA for disaster preparedness, response, and recovery. TwiSA has also effectively deployed on a large number of tweets and tracks the negative concerns of people during the 2015 South Carolina flood.

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Section: Related Workmentioning

confidence: 99%

Twitter speaks: A case of national disaster situational awareness

Karami

Shah

Vaezi

et al. 2019

Journal of Information Science

116

View full text Add to dashboard Cite

show abstract

“…In comparative studies presented in [5,17,19,20], which were performed by using tweets or reviews datasets, the evaluation of results was made only in terms of accuracy, however the processing time was not considered. Regarding the continuously expanding size and complexity of big data in the future, it is crucial to consider both reliability and time, especially in critical systems requiring a fast response [77]. In this work, two techniques (TF-IDF and word embedding) are examined on three deep learning algorithms, which give an extended overview of performances of sentiment analysis using deep learning techniques.…”

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confidence: 99%

Sentiment Analysis Based on Deep Learning: A Comparative Study

2020

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The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users' opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features.

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“…Apple; Google; Microsoft, 27,29,34,44,55,73,75 consumer products like kindle, smart-phones, etc, 51,55,56,73,84 natural calamities, energy and environmental related, 36,46,77 cyber hatred, 47,62 entertainment 29,49,53,84 which includes tweets about music and movies, automotive or vehicles, 49,53 banking, 53 government or public campaign or public administration, 33,37,39,57,72,79 education or universities, 43,53,57,67 science or technology, 40,49,57 politics, 17,39,40,49,61,67,72,79,80,84 sports, 40,49,60 daily deals and discount, 56 trade or commercial services or business or financial...…”

Section: • Widely Used Datasets and Domains In Which The Studies For mentioning

confidence: 99%

“…Accuracy (A) It is defined as proximity of a measurement to its true value. It 17,29,40,43-45,48-50,52,55,57,60,64, is calculated as a proportion of TP and true negatives (TN) 66,[68][69][70][72][73][74][75][77][78][79]85,[81][82][83] among total inspected cases.…”

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confidence: 99%

Systematic literature review of sentiment analysis on Twitter using soft computing techniques

Kumar

Jaiswal

2019

Concurrency and Computation

134

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Sentiment detection and classification is the latest fad for social analytics on Web. With the array of practical applications in healthcare, finance, media, consumer markets, and government, distilling the voice of public to gain insight to target information and reviews is non-trivial. With a marked increase in the size, subjectivity, and diversity of social web-data, the vagueness, uncertainty and imprecision within the information has increased manifold. Soft computing techniques have been used to handle this fuzziness in practical applications. This work is a study to understand the feasibility, scope and relevance of this alliance of using Soft computing techniques for sentiment analysis on Twitter. We present a systematic literature review to collate, explore, understand and analyze the efforts and trends in a well-structured manner to identify research gaps defining the future prospects of this coupling. The contribution of this paper is significant because firstly the primary focus is to study and evaluate the use of soft computing techniques for sentiment analysis on Twitter and secondly as compared to the previous reviews we adopt a systematic approach to identify, gather empirical evidence, interpret results, critically analyze, and integrate the findings of all relevant high-quality studies to address specific research questions pertaining to the defined research domain. KEYWORDS machine learning, review, sentiment analysis, soft computing, Twitter INTRODUCTIONThe incessantly evolving dynamics of the Web in terms of the volume, velocity and variety of opinion-rich information accessible online, has made research in the domain of Sentiment Analysis (SA) a trend for many practical applications which facilitate decision support and deliver targeted information to domain analysts. Interestingly, the buzzing term ''big data'' which is estimated to be 90% unstructured 1 further makes it crucial to tap and analyze information using contemporary tools. Text mining models define the process to transform and substitute this unstructured data into a structured one for knowledge discovery. Use of classification algorithms to intelligently mine text has been studied extensively across literature. 2,3 SA, established as a typical text classification task, 4 is defined as the computational study of people's opinions, attitudes and emotions towards an entity. 5,6 It offers a technology-based solution to understand people's reactions, views and opinion polarities (positive, negative or neutral) in textual content available over social media sources.Research studies and practical applications in the field of SA have escalated in the past decade with the transformation and expansion of Web from passive provider of content to an active socially-aware distributor of collective intelligence. This new collaborative Web (called Web 2.0), 7 extended by Web-based technologies like comments, blogs and wikis, social media portals like Twitter or Facebook, that allow to build social networks based on professional relationship, i...

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Sentiment analysis during Hurricane Sandy in emergency response

Cited by 164 publications

References 18 publications

Twitter speaks: A case of national disaster situational awareness

Twitter speaks: A case of national disaster situational awareness

Sentiment Analysis Based on Deep Learning: A Comparative Study

Systematic literature review of sentiment analysis on Twitter using soft computing techniques

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