Conducting Sentiment Analysis

Lei, Lei; Liu, Dilin

doi:10.1017/9781108909679

Cited by 31 publications

(6 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Sentiment analysis is a dictionary-based text-mining technique for extracting opinions (Lei & Liu, 2021), which has been used to grasp the public sentiment towards refugees (Backfried & Shalunts, 2016;Öztürk & Ayvaz, 2018) or migration (Heidenreich et al, 2020). This chapter uses sentiment analysis to identify tendencies in the media's partisan leanings and to look at the portrayals related to them.…”

Section: The Results Of the Sentiment Analysismentioning

confidence: 99%

Media Political Leanings: Polarised Depictions of Hong Kong Migration in Taiwan

Yau

2024

Palgrave Macmillan Studies on Human Rights in Asia

View full text Add to dashboard Cite

This chapter examines the influences of the Taiwanese media’s political leaning on their coverage of Hong Kong migration by analysing news articles from the five major pro-Blue/Green printed media outlets between 2014 and 2022. Using structural topic modelling, this chapter demonstrates a politicalised trend relating to Hong Kong migration, involving two themes: migration safety and Political Security. Even though the migration safety theme has gained much more attention than the Political Security theme since the 2019 Protests in Hong Kong, the polarisation of the depiction of Hong Kong migration becomes particularly evident when issues related to the People's Republic of China (PRC) are factored in through topic comparison. In other words, compared to the pro-Blue media, which is inclined to portray the Hong Kong migration from a human rights perspective, the pro-Green media, which favours a nativist viewpoint, offer a more security-based narrative, such as cultural Sinicisation and the possibility of Chinese spies entering through Hong Kong migration. The results of sentiment analysis further highlight both media camps’ differences by detecting a positive or negative tendency.

show abstract

Section: The Results Of the Sentiment Analysismentioning

confidence: 99%

Media Political Leanings: Polarised Depictions of Hong Kong Migration in Taiwan

Yau

2024

Palgrave Macmillan Studies on Human Rights in Asia

View full text Add to dashboard Cite

show abstract

“…For emotion analysis, the sentimentr package relies on the NRC emotion lexicon [25] to compute scores of eight categories emotions in Plutchik's model of emotions [26] which includes anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. Note that although both sentiment analysis and emotion analysis are concerned with the affective dimension of human language, they work at different granularity and are designed to gauge different psycholinguistic features [27]. In addition, since sentiment and emotion…”

Section: Sentiment and Emotion Analysesmentioning

confidence: 99%

Crisis communication in the WHO COVID-19 press conferences: A retrospective analysis

Liu

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Objectives The objective of this study is to investigate, from a longitudinal perspective, how WHO communicated COVID-19 related information to the public through its press conferences during the first two years of the pandemic. Methods The transcripts of 195 WHO COVID-19 press conferences held between January 22, 2020 and February 23, 2022 were collected. All transcripts were syntactically parsed to extract highly frequent noun chunks that were potential topics of the press conferences. First-order autoregression models were fit to identify “hot” and “cold” topics. In addition, sentiments and emotions expressed in the transcripts were analyzed using lexicon-based sentiment/emotion analyses. Mann-Kendall tests were performed to capture the possible trends of sentiments and emotions over time. Results First, eleven “hot” topics were identified. These topics were pertinent to anti-pandemic measures, disease surveillance and development, and vaccine-related issues. Second, no significant trend was captured in sentiments. Last, significant downward trends were found in anticipation, surprise, anger, disgust, and fear. However, no significant trends were found in joy, trust, and sadness. Conclusions This retrospective study provided new empirical evidence on how WHO communicated issues pertaining to COVID-19 to the general public through its press conferences. With the help of the study, members of the general public, health organizations, and other stake-holders will be able to better understand the way in which WHO has responded to various critical events during the first two years of the pandemic.

show abstract

“…To be specific, sentiment analysis quantifies or extracts the polarity of sentiments, attitudes, emotions, and opinions of a given text ( Cambria, 2016 ; Rendalkar and Chandankhede, 2018 ). More importantly, sentiment analysis broadly covers two dimension, namely, sentiment analysis in a narrow sense and emotion analysis ( Lei and Liu, 2021 ). On the one hand, sentiment analysis in a narrow sense mainly identifies the sentiment polarities such as positive, negative, or neutral ( D’Andrea et al, 2015 ; Lennox et al, 2020 ; Zucco et al, 2020 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Linguistic Features and Psychological States: The Case of Virginia Woolf

2022

Front. Psychol.

View full text Add to dashboard Cite

This study investigated the relation between psychological states and linguistic features with the case of Virginia Woolf. We analyzed the data from The Diary of Virginia Woolf and Virginia Woolf: Biography by automatic text analysis and statistical analysis, including stepwise multiple regression and Deep Learning algorithm. The results suggested that the significant linguistic features can jointly predict the psychological states of Virginia Woolf, including the emotional value of anger, the absolutist word “everything,” and the total of first-person plural pronouns. In addition, we found that the total use of first-person plural pronouns and the emotional value of anger were negatively related to mental health of Virginia Woolf. While the use of the absolutist word “everything” was positively related to mental health of Virginia Woolf. Meanwhile, we developed a model that can predict the psychological states of Virginia Woolf, with 86.9% accuracy. We discussed the findings and enumerated the limitations of this study at the end of the paper. The results not only complemented previous studies in the understanding of the relation between language and psychological health, but also facilitated timely identification, intervention, and prevention of mental disorders.

show abstract

Conducting Sentiment Analysis

Cited by 31 publications

References 64 publications

Media Political Leanings: Polarised Depictions of Hong Kong Migration in Taiwan

Media Political Leanings: Polarised Depictions of Hong Kong Migration in Taiwan

Crisis communication in the WHO COVID-19 press conferences: A retrospective analysis

Linguistic Features and Psychological States: The Case of Virginia Woolf

Contact Info

Product

Resources

About