|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM358607000 |
| 003 |
DE-627 |
| 005 |
20240919232150.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2023 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1007/s11227-023-05423-9
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1539.xml
|
| 035 |
|
|
|a (DE-627)NLM358607000
|
| 035 |
|
|
|a (NLM)37359345
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Seilsepour, Azam
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Topic sentiment analysis based on deep neural network using document embedding technique
|
| 264 |
|
1 |
|c 2023
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 19.09.2024
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status Publisher
|
| 520 |
|
|
|a © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
|
| 520 |
|
|
|a Sentiment Analysis (SA) is a domain- or topic-dependent task since polarity terms convey different sentiments in various domains. Hence, machine learning models trained on a specific domain cannot be employed in other domains, and existing domain-independent lexicons cannot correctly recognize the polarity of domain-specific polarity terms. Conventional approaches of Topic Sentiment Analysis perform Topic Modeling (TM) and SA sequentially, utilizing the previously trained models on irrelevant datasets for classifying sentiments that cannot provide acceptable accuracy. However, some researchers perform TM and SA simultaneously using topic-sentiment joint models, which require a list of seeds and their sentiments from widely used domain-independent lexicons. As a result, these methods cannot find the polarity of domain-specific terms correctly. This paper proposes a novel supervised hybrid TSA approach, called Embedding Topic Sentiment Analysis using Deep Neural Networks (ETSANet), that extracts the semantic relationships between the hidden topics and the training dataset using Semantically Topic-Related Documents Finder (STRDF). STRDF discovers those training documents in the same context as the topic based on the semantic relationships between the Semantic Topic Vector, a newly introduced concept that encompasses the semantic aspects of a topic, and the training dataset. Then, a hybrid CNN-GRU model is trained by these semantically topic-related documents. Moreover, a hybrid metaheuristic method utilizing Grey Wolf Optimization and Whale Optimization Algorithm is employed to fine-tune the hyperparameters of the CNN-GRU network. The evaluation results demonstrate that ETSANet increases the accuracy of the state-of-the-art methods by 1.92%
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a CNN
|
| 650 |
|
4 |
|a GRU
|
| 650 |
|
4 |
|a Semantic similarity
|
| 650 |
|
4 |
|a Semantic topic vector
|
| 650 |
|
4 |
|a Topic modeling
|
| 650 |
|
4 |
|a Topic sentiment analysis
|
| 700 |
1 |
|
|a Ravanmehr, Reza
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Nassiri, Ramin
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t The Journal of supercomputing
|d 1998
|g (2023) vom: 05. Juni, Seite 1-39
|w (DE-627)NLM098252410
|x 0920-8542
|7 nnns
|
| 773 |
1 |
8 |
|g year:2023
|g day:05
|g month:06
|g pages:1-39
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1007/s11227-023-05423-9
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|j 2023
|b 05
|c 06
|h 1-39
|