搜索sentiment analysis共有 42 个结果
1
Dual-channel graph convolutional network with word-order knowledge for aspect-based sentiment analysis
2024年第3期 : 779-785
doi:10.19734/j.issn.1001-3695.2023.07.0310
2
Text sentiment analysis based on BERT and hypergraph with dual attention network
2024年第3期 : 786-793
doi:10.19734/j.issn.1001-3695.2023.07.0311
3
Multimodal sentiment analysis based on non-text modality reinforcement and gating fusion method
2024年第1期 : 39-44
doi:10.19734/j.issn.1001-3695.2023.04.0213
4
Aspect-level multimodal sentiment analysis based on interaction graph neural network
2023年第12期 : 3683-3689
doi:10.19734/j.issn.1001-3695.2022.10.0532
5
Consistency based graph convolution network for multimodal emotion recognition in conversation
2023年第10期 : 3100-3106
doi:10.19734/j.issn.1001-3695.2023.02.0064
6
Multimodal sentiment analysis based on modal information interaction
2023年第9期 : 2603-2608
doi:10.19734/j.issn.1001-3695.2023.02.0050
7
Multimodal sentiment analysis based on cross-modal gating mechanism and improved fusion method
2023年第7期 : 2025-2030,2038
doi:10.19734/j.issn.1001-3695.2022.12.0766
8
Sentiment analysis model of opinion triplet based on position information
2023年第3期 : 676-681
doi:10.19734/j.issn.1001-3695.2022.07.0370
9
Aspect-based sentiment analysis based on local feature focusing
2023年第3期 : 682-688
doi:10.19734/j.issn.1001-3695.2022.07.0364
10
KENAOTE: multi-task learning model for knowledge augmented aspect and opinion pair extraction
2023年第2期 : 359-364
doi:10.19734/j.issn.1001-3695.2022.07.0326
11
Chinese aspect-based sentiment analysis integrating sentiment and topic information
2022年第12期 : 3614-3619,3625
doi:10.19734/j.issn.1001-3695.2022.05.0236
12
Handling contrastive sentences in sentiment analysis based on attention network
2022年第9期 : 2695-2700,2716
doi:10.19734/j.issn.1001-3695.2022.02.0052
13
Click prediction model based on product description
2022年第8期 : 2422-2426
doi:10.19734/j.issn.1001-3695.2022.01.0025
14
Aspect based sentiment analysis with progressive enhancement and graph convolution
2022年第7期 : 2037-2042
doi:10.19734/j.issn.1001-3695.2022.01.0005
15
Aspect level sentiment analysis with dependency tree enhanced attention model
2022年第6期 : 1656-1662
doi:10.19734/j.issn.1001-3695.2021.12.0629
16
MASGC: simple graph convolutional emotion analysis model with specific masking
2022年第4期 : 1049-1053
doi:10.19734/j.issn.1001-3695.2021.09.0400
17
Aspect-level sentiment analysis based on double-layer part-of-speech-aware and multi-head interactive attention mechanism
2022年第3期 : 704-710
doi:10.19734/j.issn.1001-3695.2021.08.0340
18
Multimodal sentiment analysis based on hybrid feature fusion of multi-level attention mechanism and multi-task learning
2022年第3期 : 716-720
doi:10.19734/j.issn.1001-3695.2021.08.0357
19
Aspect-based sentiment analysis based on attention network
2022年第2期 : 411-416
doi:10.19734/j.issn.1001-3695.2021.07.0297
20
Chinese online comments sentiment analysis based on weighted char-word mixture word representation
2022年第1期 : 31-36
doi:10.19734/j.issn.1001-3695.2021.06.0253
21
Combining dependency syntactic parsing with interactive attention mechanism for implicit aspect extraction
2022年第1期 : 37-42
doi:10.19734/j.issn.1001-3695.2021.06.0249
22
Aspect-based sentiment analysis based on graph neural network
2021年第12期 : 3574-3580,3585
doi:10.19734/j.issn.1001-3695.2021.05.0166
23
Aspect level sentiment analysis based on distance and graph convolution network
2021年第11期 : 3274-3278,3321
doi:10.19734/j.issn.1001-3695.2021.04.0150
24
Soft prototype enhanced adaptive loss model for aspect extraction
2021年第11期 : 3310-3315
doi:10.19734/j.issn.1001-3695.2021.04.0101
25
Chinese text sentiment analysis based on ELMo and Bi-SAN
2021年第8期 : 2303-2307
doi:10.19734/j.issn.1001-3695.2020.12.0543
26
Multi-relationship collaborative learning model for aspect extraction
2021年第8期 : 2328-2333
doi:10.19734/j.issn.1001-3695.2020.12.0412
27
Joint left and right attention mechanism for aspect-level text sentiment analysis
2021年第6期 : 1753-1758
doi:10.19734/j.issn.1001-3695.2020.07.0185
28
End-to-end multi-hop memory network for aspect-level sentiment analysis
2021年第5期 : 1409-1415,1427
doi:10.19734/j.issn.1001-3695.2020.06.0110
29
Research on image sentiment analysis based on multi-visual object fusion
2021年第4期 : 1250-1255
doi:10.19734/j.issn.1001-3695.2020.02.0087
30
Visual analysis of movie reviews sentiment for box office forecasting
2020年第10期 : 2945-2950
doi:10.19734/j.issn.1001-3695.2019.05.0196
31
Chinese text sentiment analysis based on character-level two-channel composite network
2020年第9期 : 2674-2678
doi:10.19734/j.issn.1001-3695.2019.04.0121
32
Multimedia sentiment analysis on microblog based on multi-feature fusion
2020年第7期 : 1935-1939,1951
doi:10.19734/j.issn.1001-3695.2018.12.0929
33
Text sentiment analysis based on hybrid mutual information algorithm
2020年第2期 : 337-341
doi:10.19734/j.issn.1001-3695.2018.08.0537
34
Personality-based microblog sentiment analysis model PLSTM
2020年第2期 : 342-346
doi:10.19734/j.issn.1001-3695.2018.07.0521
35
Target-specific sentiment analysis based on CRT mechanism hybrid neural network
2020年第2期 : 360-364
doi:10.19734/j.issn.1001-3695.2018.08.0538
36
Sentiment classification depth model based on word2vec and bi-directional LSTM
2019年第12期 : 3583-3587,3596
doi:10.19734/j.issn.1001-3695.2018.08.0599
37
Text sentiment analysis based on recurrent neural networks and attention model
2019年第11期 : 3282-3285
doi:10.19734/j.issn.1001-3695.2018.05.0300
38
Emoji-attentional neural network for microblog sentiment analysis
2019年第9期 : 2647-2650
doi:10.19734/j.issn.1001-3695.2018.03.0152
39
Cross-grained sentiment analysis oriented to college student microblog
2019年第6期 : 1618-1622
doi:10.19734/j.issn.1001-3695.2017.12.0815
40
Sentiment analysis of micro-blog based on CNN and Tree-LSTM
2019年第5期 : 1371-1375
doi:10.19734/j.issn.1001-3695.2017.11.0735
41
Deeper attention-based LSTM for aspect sentiment analysis
2019年第4期 : 1075-1079
doi:10.19734/j.issn.1001-3695.2017.11.0736
42
Analysis of structure characteristics of high frequency word co-occurrence network of online shopping reviews
2019年第1期 : 53-57
doi:10.19734/j.issn.1001-3695.2017.06.0657