Systematic literature review on context-based sentiment analysis in social multimedia

Kumar, Akshi; Garg, Geetanjali

doi:10.1007/s11042-019-7346-5

Cited by 54 publications

(33 citation statements)

References 46 publications

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“…The variable measuring three sentiment tones (positive, neutral, and negative) was based on using the terms “joy,” “happy,” and “like” as points of reference for a positive tone, while “anger,” “fear,” and “sadness” were used as points of reference for a negative tone [ 22 , 25 , 26 ]. The synonyms of emotion words and adjectives not associated with the aforementioned emotions were labelled as having a neutral tone [ 25 - 27 , 30 , 43 ]. Negative sentiments are ideal indicators of collective perceptual bias in measuring the blaming of accused culprits [ 14 , 19 - 23 ].…”

Section: Methodsmentioning

confidence: 99%

Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques

Chang¹,

Schulz²,

Tu³

et al. 2020

J Med Internet Res

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Background Information about a new coronavirus emerged in 2019 and rapidly spread around the world, gaining significant public attention and attracting negative bias. The use of stigmatizing language for the purpose of blaming sparked a debate. Objective This study aims to identify social stigma and negative sentiment toward the blameworthy agents in social communities. Methods We enabled a tailored text-mining platform to identify data in their natural settings by retrieving and filtering online sources, and constructed vocabularies and learning word representations from natural language processing for deductive analysis along with the research theme. The data sources comprised of ten news websites, eleven discussion forums, one social network, and two principal media sharing networks in Taiwan. A synthesis of news and social networking analytics was present from December 30, 2019, to March 31, 2020. Results We collated over 1.07 million Chinese texts. Almost two-thirds of the texts on COVID-19 came from news services (n=683,887, 63.68%), followed by Facebook (n=297,823, 27.73%), discussion forums (n=62,119, 5.78%), and Instagram and YouTube (n=30,154, 2.81%). Our data showed that online news served as a hotbed for negativity and for driving emotional social posts. Online information regarding COVID-19 associated it with China—and a specific city within China through references to the “Wuhan pneumonia”—potentially encouraging xenophobia. The adoption of this problematic moniker had a high frequency, despite the World Health Organization guideline to avoid biased perceptions and ethnic discrimination. Social stigma is disclosed through negatively valenced responses, which are associated with the most blamed targets. Conclusions Our sample is sufficiently representative of a community because it contains a broad range of mainstream online media. Stigmatizing language linked to the COVID-19 pandemic shows a lack of civic responsibility that encourages bias, hostility, and discrimination. Frequently used stigmatizing terms were deemed offensive, and they might have contributed to recent backlashes against China by directing blame and encouraging xenophobia. The implications ranging from health risk communication to stigma mitigation and xenophobia concerns amid the COVID-19 outbreak are emphasized. Understanding the nomenclature and biased terms employed in relation to the COVID-19 outbreak is paramount. We propose solidarity with communication professionals in combating the COVID-19 outbreak and the infodemic. Finding solutions to curb the spread of virus bias, stigma, and discrimination is imperative.

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Section: Methodsmentioning

confidence: 99%

Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques

Chang¹,

Schulz²,

Tu³

et al. 2020

J Med Internet Res

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“…The systematic review conducted in this paper is based on studies [27]- [29], [61]. They have divided the investigation into the planning phase, executing phase, and finally reporting phase.…”

Section: Methodology For a Systematic Reviewmentioning

confidence: 99%

Acoustic Modeling in Speech Recognition: A Systematic Review

Bhatt¹,

Jain²,

Dev³

2020

IJACSA

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The paper presents a systematic review of acoustic modeling (AM) techniques in speech recognition(SR). Acoustic modeling establishes a relationship between acoustic information and language construct in SR. Over the past decades, researchers presented studies addressing specific concerns in AM. However, all previous research works lack a systematic and comprehensive review of acoustic modeling issues. A systematic review is introduced to understand the acoustic modeling issues in speech recognition. This paper provides an extensive and comprehensive inspection of various researches that have been performed since 1984. The extensive investigation and analysis into AM was performed by getting the relevant data from 73 research works chose after the screening process between the years from 1984 to 2020. The systematic review process was divided into different parts to investigate acoustic modeling issues. Main issues in acoustic modeling such as feature extraction techniques, acoustic modeling units, speech corpora, classification methods, different tools used, language issues applied, and evaluation parameters were investigated. This study helps the reader to understand various acoustic modeling issues with comprehensive details. The research outcomes presented in this study depict research trends and shed light on new research topics in AM. The result of this review can be used to build a better speech recognition system by choosing a suitable acoustic modeling construct in SR.

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“…According to researches Li, Jin & Quan (2020), Kumar & Garg (2019) and Ren, Wu & Liu (2018) to text sentiment analysis (SA) are part of the field of natural language processing (NLP). A series of methods are proposed for online data like product review analysis, social network analysis, public opinion monitoring, movie box office forecasts, stock market fluctuation trend forecasts, etc (Ali et al, 2019;Ali et al, 2020;Basiri et al, 2020;.…”

Section: Text Sentiment Analysismentioning

confidence: 99%

LSTM-based sentiment analysis for stock price forecast

Chang

2021

PeerJ Computer Science

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Investing in stocks is an important tool for modern people’s financial management, and how to forecast stock prices has become an important issue. In recent years, deep learning methods have successfully solved many forecast problems. In this paper, we utilized multiple factors for the stock price forecast. The news articles and PTT forum discussions are taken as the fundamental analysis, and the stock historical transaction information is treated as technical analysis. The state-of-the-art natural language processing tool BERT are used to recognize the sentiments of text, and the long short term memory neural network (LSTM), which is good at analyzing time series data, is applied to forecast the stock price with stock historical transaction information and text sentiments. According to experimental results using our proposed models, the average root mean square error (RMSE ) has 12.05 accuracy improvement.

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Systematic literature review on context-based sentiment analysis in social multimedia

Cited by 54 publications

References 46 publications

Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques

Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques

Acoustic Modeling in Speech Recognition: A Systematic Review

LSTM-based sentiment analysis for stock price forecast

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