Polarity Classification of Public Health Opinions in Chinese

Zhang, Changli; Zeng, Daniel; Xu, Qingyang; Xin, Xueling; Mao, Wenji; Wang, Fei‐Yue

doi:10.1007/978-3-540-69304-8_47

Cited by 7 publications

(6 citation statements)

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“…Additionally, the boundaries between degree adverbs and intensifiers can sometimes be blurred, and so it is not surprising that the terms are occasionally used interchangeably. Impacting propositional content or not, both degree adverbs and intensifiers impact the sentiment of the predicate, and there is some work in exploring this interaction (Zhang, Zeng, Xu, Xin, Mao, & Wang, 2008;Xu, Wong, Lu, Xia, & Li, 2008;Lu & Tsou, 2010;Taboada, Voll, & Brooke, 2008). Most of this work focuses on identifying sentiment words by bootstrapping over patterns involving degree adverbs and intensifiers.…”

Section: Degree Adverbs Intensifiers Modalsmentioning

confidence: 99%

Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Text

Mohammad

2020

Preprint

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Recent advances in machine learning have led to computer systems that are humanlike in behaviour. Sentiment analysis, the automatic determination of emotions in text, is allowing us to capitalize on substantial previously unattainable opportunities in commerce, public health, government policy, social sciences, and art. Further, analysis of emotions in text, from news to social media posts, is improving our understanding of not just how people convey emotions through language but also how emotions shape our behaviour. This article presents a sweeping overview of sentiment analysis research that includes: the origins of the field, the rich landscape of tasks, challenges, a survey of the methods and resources used, and applications. We also discuss discuss how, without careful fore-thought, sentiment analysis has the potential for harmful outcomes. We outline the latest lines of research in pursuit of fairness in sentiment analysis.

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Section: Degree Adverbs Intensifiers Modalsmentioning

confidence: 99%

Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Text

Mohammad

2020

Preprint

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“…Each of these steps provides a procedure of Identification, Designing, Evaluating and Testing. Sentiment analysis approaches were also realized for: English National Health Service website comments [100], on-line forums, blogs and news comments [101] with an additional multi-steps algorithm [102], Chinese reviews on 'euthanasia' from various Web pages, Blog postings, and online forums [103], English-language Internet conversations (ICs) regarding prostate cancer treatment with active surveillance (AS) [104]; for medical domain sentiment lexicon creation and evaluations [105], the drug review dataset using Artificial Neural Networks algorithms [106]. In [107], the TagCrowd tool for unigrams and Many Eyes tool for bigrams retrieving were used to analyse the patient experience of primary care.…”

Section: Linguistic -Based and Nlp Approaches For Analysing The Responses From Free-text Questionnairesmentioning

confidence: 99%

Enabling Deeper Linguistic-Based Text Analytics—Construct Development for the Criticality of Negative Service Experience

Ojo

Rizun

2019

IEEE Access

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Significant progress has been made in linguistic-based text analytics particularly with the increasing availability of data and deep learning computational models for more accurate opinion analysis and domain-specific entity recognition. In understanding customer service experience from texts, analysis of sentiments associated with different stages of the service lifecycle is a useful starting point. However, when richer insights into issues associated with negative sentiments and experiences are desired to inform intervention, deeper linguistic analyses such as identifying specific touchpoints and the context of the service users become important. While research in this direction is beginning to emerge in some domains, we are yet to see similar efforts in the domain of healthcare. We present in this paper the results from our construct development effort for quantifying how critical a negative patient experience is using different elements of the available textual feedback as a key basis for prioritizing interventions by service providers. This involves the identification of the different dimensions of the construct, associated linguistic markers and metrics to compute the criticality index. We also present the results of the application of our developed conceptualization to linguistic-based text analysis of a small dataset of patient experience feedback.INDEX TERMS Customer experience, construct development, linguistic analysis, intensity markers, negative event, magnitude of consequences.

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“…E-HowNet is a lexical semantic representation model extended from HowNet to define lexical senses and achieve compositional semantics. In general, Chinese text is subtler and can lead to a higher degree of ambiguity than English text (Zhang et al, 2008). Among other things, Zhang et al (2008) argued that Chinese opinion mining poses some challenges, including: an additional step of segmentation is required; the comparative…”

Section: Opinion Miningmentioning

confidence: 99%

“…Web mining for the mayoral election prediction and superlative sentences of Chinese using degree adverbs is much complicated than English sentences which mainly rely upon the suffix "er" or "est"; compared with English, Chinese opinion mining poses greater challenge for lack of adequate Natural Language Processing resources and corpus (Su et al, 2008); and negative reviews often contain many apparently positive phrases even if their authors maintain a strong negative tone (Zhang et al, 2008).…”

mentioning

confidence: 99%

“…In their research to mine all the customer reviews of a product, Hu and Liu (2004) observed that an opinion word associated with an attribute word will usually occur in its vicinity; hence, they proposed to use the adjectives near the attribute words as the opinion words. However, Chinese text is subtler and can lead to a higher degree of ambiguity than English text (Zhang et al, 2008). Popescu and Etzioni (2007) used similar ideas to identify potential opinion phrases.…”

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confidence: 99%

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Web mining for the mayoral election prediction in Taiwan

Huang

2017

AJIM

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Purpose The prediction of pre-election polls is an issue of concern for both politicians and voters. The Taiwan nine-in-one election held in 2014 ended with jaw-dropping results; apparently, traditional polls did not work well. As a remedy to this problem, the purpose of this paper is to utilize the comments posted on social media to analyze civilians’ views on the two candidates for mayor of Taichung City, Chih-chiang Hu, and Chia-Lung Lin. Design/methodology/approach After conducting word segmentation and part-of-speech tagging for the collected reviews, this study constructs the opinion phrase extraction rules for identifying the opinion words associated with the attribute words. Next, this study classifies the attribute words into six municipal governance-related topics and calculates the opinion scores for each candidate. Finally, this study uses correspondence analysis to transform opinion information on the candidates into a graphical display to facilitate the interpretation of voters’ views. Findings The results show that the topics of candidates’ backgrounds and transport infrastructure were the two most critical factors for the election prediction. Based on the predication, Lin outscores Hu by 17.74 percent which is close to the real election results. Research limitations/implications This study proposes new rules for the extraction of Chinese opinion words associated with attribute words. Practical implications This study applies Chinese semantic analysis to assist in predicting election results and investigating the topics of concern to voters. Originality/value The proposed opinion phrase extraction rules for Chinese social media, as well as the election forecast process, can provide valuable references for political parties and candidates to plan better nomination and election strategies.

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Polarity Classification of Public Health Opinions in Chinese

Cited by 7 publications

References 3 publications

Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Text

Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Text

Enabling Deeper Linguistic-Based Text Analytics—Construct Development for the Criticality of Negative Service Experience

Web mining for the mayoral election prediction in Taiwan

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