Concept-Level Sentiment Analysis with SenticNet

Bisio, Federica; Meda, Claudia; Gastaldo, Paolo; Zunino, Rodolfo; Wang, Zhaoxia

doi:10.1007/978-3-319-55394-8_9

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…Strength of review sentiment was processed by a Python program, which uses the SenticNet3 API and SnowNLP to extract concepts of text and compute the sentiment strength score. As a knowledge base, the SenticNet3 provides a set of semantics, polarity and sentics correlated with 100,000 human language concepts (Bisio et al, 2017). The SnowNLP is a Python-based library for classifying, tagging, stemming, parsing and semantic reasoning of Chinese text contents (Wang et al, 2018).…”

Section: Variables and Measurementsmentioning

confidence: 99%

Understanding online review helpfulness in omnichannel retailing

Yang

Zhou

Yao

et al. 2019

IMDS

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Purpose As retailers have increasingly embraced an omnichannel retailing strategy, explaining and predicting the helpfulness of online review should consider both online-based and offline-based reviews. The paper aims to discuss this issue. Design/methodology/approach Based on the signaling theory, this study intends to examine the impacts of review-related and reviewer-related signals on review helpfulness in the context of omnichannel retailing. The proposed research model and corresponding hypotheses were tested by using negative binomial regression. Findings The results shown that both review-related (review rating and review sentiment strength) and reviewer-related (reviewer real name and reviewer expertise) signals positively affect review helpfulness. Contrary to the authors’ expectations, review length negatively affects review helpfulness. Specifically, when the review submitted from an omnichannel retailer’s offline channel, the positive impacts of reviewer real name on review helpfulness will be stronger, and the positive impacts of reviewer expertise on review helpfulness will be weaker. Originality/value Unlike many previous studies tend to explore the antecedents of review helpfulness in a single-channel setting, the study examined the factors that affect review helpfulness in an omnichannel retailing context.

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Section: Variables and Measurementsmentioning

confidence: 99%

Understanding online review helpfulness in omnichannel retailing

Yang

Zhou

Yao

et al. 2019

IMDS

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“…Nevertheless, as sentiment analysis can be carried out at the document, sentence or aspect level (Liu, 2020), it can possibly go further to include the document-level sentiment analysis. Some other scholars also proposed concept-level analysis to complement current domaindependent sentiment analysis (Bisio et al, 2017). As a matter of fact, the article has eliminated such important information as emoticons from analysis, which makes the document analysis imcomplete.…”

Section: Data Analysismentioning

confidence: 99%

How do Chinese students effectively convey emotions through the discussion forums in the LMOOCs?

Chen

Zheng²

2023

Front. Psychol.

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“…SenticNet A concept-level sentiment analysis framework that assigns sentiment scores to concepts based on their semantic orientation, conceptual polarity, and semantic relatedness to other concepts. [39,40] AFINN A list of English words rated for valence with an integer between minus five (negative) and plus five (positive).…”

Section: Sentiwordnetmentioning

confidence: 99%

Emotions Matter: A Systematic Review and Meta-Analysis of the Detection and Classification of Students’ Emotions in STEM during Online Learning

Anwar,

Rehman,

Nasralla

et al. 2023

Education Sciences

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In recent years, the rapid growth of online learning has highlighted the need for effective methods to monitor and improve student experiences. Emotions play a crucial role in shaping students’ engagement, motivation, and satisfaction in online learning environments, particularly in complex STEM subjects. In this context, sentiment analysis has emerged as a promising tool to detect and classify emotions expressed in textual and visual forms. This study offers an extensive literature review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) technique on the role of sentiment analysis in student satisfaction and online learning in STEM subjects. The review analyses the applicability, challenges, and limitations of text- and facial-based sentiment analysis techniques in educational settings by reviewing 57 peer-reviewed research articles out of 236 articles, published between 2015 and 2023, initially identified through a comprehensive search strategy. Through an extensive search and scrutiny process, these articles were selected based on their relevance and contribution to the topic. The review’s findings indicate that sentiment analysis holds significant potential for improving student experiences, encouraging personalised learning, and promoting satisfaction in the online learning environment. Educators and administrators can gain valuable insights into students’ emotions and perceptions by employing computational techniques to analyse and interpret emotions expressed in text and facial expressions. However, the review also identifies several challenges and limitations associated with sentiment analysis in educational settings. These challenges include the need for accurate emotion detection and interpretation, addressing cultural and linguistic variations, ensuring data privacy and ethics, and a reliance on high-quality data sources. Despite these challenges, the review highlights the immense potential of sentiment analysis in transforming online learning experiences in STEM subjects and recommends further research and development in this area.

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Concept-Level Sentiment Analysis with SenticNet

Cited by 9 publications

References 31 publications

Understanding online review helpfulness in omnichannel retailing

Understanding online review helpfulness in omnichannel retailing

How do Chinese students effectively convey emotions through the discussion forums in the LMOOCs?

Emotions Matter: A Systematic Review and Meta-Analysis of the Detection and Classification of Students’ Emotions in STEM during Online Learning

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