Identifying critical outbreak time window of controversial events based on sentiment analysis

Wang, Mingyang; Wu, Huan; Zhang, Tianyu; Zhu, Shengqing

doi:10.1371/journal.pone.0241355

Cited by 5 publications

(6 citation statements)

References 59 publications

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“…Under an almost identical general idea, Wang et al. ( 2020 ) aimed to determine the critical time window of public opinion concerning an event, by applying multi-emotional sentiment classification to microblog posts in Sina Weibo (published within a short time period of approximately 10 days after certain events). The volume of tweets per class (among the employed 7 emotion classes) was simply examined to find out that monitoring the negative emotions trend is crucial for predicting the influence of events.…”

Section: Related Workmentioning

confidence: 99%

“…( 2013 ) (expect, joy, love, surprise, anxiety, sorrow, angry and hate), Wang et al. ( 2020 ) (happiness, like, sadness, disgust, astonishment, anger, and fear) and El Barachi et al. ( 2021 ) (joy, inspiration, anger, discrimination, support).…”

Section: Related Workmentioning

confidence: 99%

“…( 2019 ), Wang et al. ( 2020 ) uses an emotional dictionary Zhang et al. ( 2018 ) and a negative word dictionary, Yaqub et al.…”

Section: Related Workmentioning

confidence: 99%

“…Only a few rely on modern DNN-based word representation schemes, such as Wang et al. ( 2020 ) and El Barachi et al. ( 2021 ) which exploit Word2Vec (Mikolov et al.…”

Section: Related Workmentioning

confidence: 99%

“…Almost all of the presented methods utilize outdated approaches, such as Wang et al. ( 2020 ), Tavoschi et al. ( 2020 ) or Onyenwe et al.…”

Section: Related Workmentioning

confidence: 99%

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Public opinion monitoring through collective semantic analysis of tweets

Karamouzas

Mademlis

Pitas

2022

Soc. Netw. Anal. Min.

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The high popularity of Twitter renders it an excellent tool for political research, while opinion mining through semantic analysis of individual tweets has proven valuable. However, exploiting relevant scientific advances for collective analysis of Twitter messages in order to quantify general public opinion has not been explored. This paper presents such a novel, automated public opinion monitoring mechanism, consisting of a semantic descriptor that relies on Natural Language Processing algorithms. A four-dimensional descriptor is first extracted for each tweet independently, quantifying text polarity, offensiveness, bias and figurativeness. Subsequently, it is summarized across multiple tweets, according to a desired aggregation strategy and aggregation target. This can then be exploited in various ways, such as training machine learning models for forecasting day-by-day public opinion predictions. The proposed mechanism is applied to the 2016/2020 US Presidential Elections tweet datasets and the resulting succinct public opinion descriptions are explored as a case study.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…( 2019 ), Wang et al. ( 2020 ) uses an emotional dictionary Zhang et al. ( 2018 ) and a negative word dictionary, Yaqub et al.…”

Section: Related Workmentioning

confidence: 99%

“…Only a few rely on modern DNN-based word representation schemes, such as Wang et al. ( 2020 ) and El Barachi et al. ( 2021 ) which exploit Word2Vec (Mikolov et al.…”

Section: Related Workmentioning

confidence: 99%

“…Almost all of the presented methods utilize outdated approaches, such as Wang et al. ( 2020 ), Tavoschi et al. ( 2020 ) or Onyenwe et al.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Public opinion monitoring through collective semantic analysis of tweets

Karamouzas

Mademlis

Pitas

2022

Soc. Netw. Anal. Min.

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Social media sentiment analysis and opinion mining in public security: Taxonomy, trend analysis, issues and future directions

Md Suhaimin,

Ahmad Hijazi,

Moung

et al. 2023

Journal of King Saud University - Computer and Information Scie

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DRMM: A novel data mining-based emotion transfer detecting method for emotion prediction of social media

Shi

Xue

Yin

et al. 2022

Journal of Information Science

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With the progress of the Internet and information technology, emotion analysis has been applied to analyse the emotional orientation and evolution trend of online public opinion of online tweets. At present, most of the existing methods use econometric model and machine learning algorithm to predict the trend of online public opinion. Although these methods have achieved good prediction results, they do not take into account the influence of internal factors on network public opinion prediction, such as mutual migration among emotion classes. The emotion may change dynamically because different events trigger it in the evolution process. In this view, this article proposes a novel method, called Deviation Rule Markov Model (DRMM), to predict the emotional change trend of Internet users in online public opinion by analysing the correlation between Internet users’ emotional categories. Structurally, the proposed DRMM involves various processes such as pre-processing, emotion classification, data mining and transfer prediction. For the processing of network comment data, the proposed model initially undergoes pre-processing to delete unnecessary data. Then, the extended fuzzy emotion ontology is used to annotate the emotion class of the comment data. Besides, an extended association rule mining algorithm is used in the emotion association analysis process to obtain the transfer probability between emotion classes. Moreover, Markov chain is used to construct an emotional state transition matrix to predict the transition probability of positive or negative emotions. According to the predicted single emotion transfer probability results, the analytic hierarchy process is used to assign values to different emotion classes, and finally, the transfer probability of the overall emotion in a certain period is obtained. Compared with the actual case, the mean absolute error (MAE) and root mean square error (RMSE) of the proposed model are 2.7119 and 3.7254, respectively, which has good prediction performance.

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Identifying critical outbreak time window of controversial events based on sentiment analysis

Cited by 5 publications

References 59 publications

Public opinion monitoring through collective semantic analysis of tweets

Public opinion monitoring through collective semantic analysis of tweets

Social media sentiment analysis and opinion mining in public security: Taxonomy, trend analysis, issues and future directions

DRMM: A novel data mining-based emotion transfer detecting method for emotion prediction of social media

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