Ten Years of Sentic Computing

Susanto, Yosephine; Wang, Zhaoxia; Ng, Bee Chin; Hussain, Amir

doi:10.1007/s12559-021-09824-x

Cited by 26 publications

(10 citation statements)

References 131 publications

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“…In order to assess the feasibility of the affective classifications provided by our system, and crucial for the above presented recommendation part, we also compared the results of DEGARI 2.0 with SenticNet7 (Cambria et al (2022), (Susanto et al (2022): a state of the art emotion extraction system that employs a plethora of neural language models and that is able to classify both basic and complex emotions since it relies on an extension of Plutchik's model called the Hourglass model (Susanto et al (2020).…”

Section: Degari 20 Vs Human Annotations and Senticnet 7: Results And ...mentioning

confidence: 99%

DEGARI 2.0: A diversity-seeking, explainable, and affective art recommender for social inclusion

Lieto

Pozzato

Striani

et al. 2023

Cognitive Systems Research

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Section: Degari 20 Vs Human Annotations and Senticnet 7: Results And ...mentioning

confidence: 99%

DEGARI 2.0: A diversity-seeking, explainable, and affective art recommender for social inclusion

Lieto

Pozzato

Striani

et al. 2023

Cognitive Systems Research

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“…Another example of more explainable models is the ensembles of symbolic and subsymbolic AI. Models like the Sentic LSTM [125] may be the start for new explainable models for ABSA [185].…”

Section: Discussionmentioning

confidence: 99%

A Survey on Aspect-Based Sentiment Classification

Brauwers

Frăsincar

2022

ACM Comput. Surv.

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With the constantly growing number of reviews and other sentiment-bearing texts on the Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect-based sentiment classification (ABSC) allows for the automatic extraction of highly fine-grained sentiment information from text documents or sentences. In this survey, the rapidly evolving state of the research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the ABSC models into three major categories: knowledge-based, machine learning, and hybrid models. This taxonomy is accompanied with summarizing overviews of the reported model performances, and both technical and intuitive explanations of the various ABSC models. State-of-the-art ABSC models are discussed, such as models based on the transformer model, and hybrid deep learning models that incorporate knowledge bases. Additionally, various techniques for representing the model inputs and evaluating the model outputs are reviewed. Furthermore, trends in the research on ABSC are identified and a discussion is provided on the ways in which the field of ABSC can be advanced in the future.

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“…Many researchers have attempted to address the problem of ambiguity and subjectivity (Sindhu et al, 2021;Das and Sagnika, 2020). A comprehensive view of the models, resources, algorithms, and applications evolved in the context of sentiment analysis and affective computing has been introduced by the authors (Susanto et al, 2021).…”

Section: Sentiment Analysismentioning

confidence: 99%

Fine-grained Sentiment-enhanced Collaborative Filtering-based Hybrid Recommender System

Alatrash,

Priyadarshini

2024

JWE

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Developing online educational platforms necessitates the incorporation of new intelligent procedures in order to improve long-term student experience. Presently, e-learning recommender systems rely on deep learning methods to recommend appropriate e-learning materials to the students based on their learner profiles. Fine-grained sentiment analysis (FSA) can be leveraged to enrich the recommender system. User-posted reviews and rating data are vital in accurately directing the student to the appropriate e-learning resources based on posted comments by comparable learners. In this work, a new e-learning recommendation system is proposed based on individualization and FSA. A hybrid framework is provided by integrating alternating least square (ALS) based collaborative filtering (CF) with FSA to generate an effective e-content recommendation named HCFSAR. ALS attempts to capture the learner’s latent factors based on their selections of interest to build the learner profile. Three FSA models based on attention mechanisms and bidirectional long short-term memory (bi-LSTM) are suggested and used to train twelve models in order to predict new ratings from learner-posted book reviews based on the extracted learner profile. HCFSAR used multiplication word embeddings for stronger corpus representation that were trained on a dataset generated for an educational context and showed a better accuracy of 93.39% for the best model entitled MHAM based ABHR-2 with multiplication (MHAAM), which performed better than other models. A tailored dataset that has been created by scraping reviews of different e-learning resources is leveraged to train different proposed models and validate against public datasets.

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Ten Years of Sentic Computing

Cited by 26 publications

References 131 publications

DEGARI 2.0: A diversity-seeking, explainable, and affective art recommender for social inclusion

DEGARI 2.0: A diversity-seeking, explainable, and affective art recommender for social inclusion

A Survey on Aspect-Based Sentiment Classification

Fine-grained Sentiment-enhanced Collaborative Filtering-based Hybrid Recommender System

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