Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Qianhua Cai"'
Publikováno v:
Frontiers in Physics, Vol 12 (2024)
Introduction: Aspect-based sentiment classification is a fine-grained sentiment classification task. State-of-the-art approaches in this field leverage graph neural networks to integrate sentence syntax dependency. However, current methods fail to ex
Externí odkaz:
https://doaj.org/article/df1527e9cb234e719bd8773185c90430
Publikováno v:
CAAI Transactions on Intelligence Technology, Vol 7, Iss 4, Pp 710-720 (2022)
Abstract Sequence labelling (SL) tasks are currently widely studied in the field of natural language processing. Most sequence labelling methods are developed on a large amount of labelled training data via supervised learning, which is time‐consum
Externí odkaz:
https://doaj.org/article/faba7d098cfb40e6b3ef444387ae22eb
Publikováno v:
Mathematics, Vol 12, Iss 2, p 317 (2024)
Sarcasm represents a language form where a discrepancy lies between the literal meanings and implied intention. Sarcasm detection is challenging with unimodal text without clearly understanding the context, based on which multimodal information is in
Externí odkaz:
https://doaj.org/article/321b92dff8f74ca5a6c8354fcc5ca8df
Publikováno v:
Mathematics, Vol 11, Iss 18, p 3877 (2023)
Aspect-level sentiment classification (ALSC) is a fine-grained sentiment analysis task that aims to predict the sentiment of the given aspect in a sentence. Recent studies mainly focus on using the Graph Convolutional Networks (GCN) to deal with both
Externí odkaz:
https://doaj.org/article/cfb081ec574a44ec962ce2633a67fa2c
Publikováno v:
Mathematics, Vol 11, Iss 10, p 2335 (2023)
An increasing number of people tend to convey their opinions in different modalities. For the purpose of opinion mining, sentiment classification based on multimodal data becomes a major focus. In this work, we propose a novel Multimodal Interactive
Externí odkaz:
https://doaj.org/article/54e78a471b8e46788f0609b9783e86bb
Publikováno v:
Mathematics, Vol 10, Iss 20, p 3908 (2022)
ALSC (Aspect-level Sentiment Classification) is a fine-grained task in the field of NLP (Natural Language Processing) which aims to identify the sentiment toward a given aspect. In addition to exploiting the sentence semantics and syntax, current ALS
Externí odkaz:
https://doaj.org/article/0d3e15adb416435f964c5bb656725524
Publikováno v:
International Journal of Data Warehousing and Mining. 19:1-20
The main task of aspect-based sentiment analysis is to determine the sentiment polarity of a given aspect in the sentence. A major issue lies in identifying the aspect sentiment is to establish the relationship between the aspect and its opinion word
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::47f397b5b3a56e0411775162ddb34786
https://doi.org/10.4018/ijdwm.321107
https://doi.org/10.4018/ijdwm.321107
Publikováno v:
International Journal of Data Warehousing and Mining. 19:1-15
Aspect-based sentiment analysis (ABSA) aims to classify the sentiment polarity of a given aspect in a sentence or document, which is a fine-grained task of natural language processing. Recent ABSA methods mainly focus on exploiting the syntactic info
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d6a17f3c7488fe80326dedc69c8eee4
https://doi.org/10.4018/ijdwm.319803
https://doi.org/10.4018/ijdwm.319803
Semi-Supervised Sentiment Classification on E-Commerce Reviews Using Tripartite Graph and Clustering
Publikováno v:
International Journal of Data Warehousing and Mining. 18:1-20
Sentiment classification constitutes an important topic in the field of Natural Language Processing, whose main purpose is to extract the sentiment polarity from unstructured texts. The label propagation algorithm, as a semi-supervised learning metho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f8df8b0816678c8ee04fda57d5d72fb0
https://doi.org/10.4018/ijdwm.307904
https://doi.org/10.4018/ijdwm.307904
Publikováno v:
International Journal of Machine Learning and Cybernetics.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ddccd0cb856963c0935accbb19ba23f0
https://doi.org/10.1007/s13042-023-01831-8
https://doi.org/10.1007/s13042-023-01831-8