Aspect-Level Sentiment Analysis Based on Deep Learning
Source abstract
Abstract
In recent years, deep learning methods have developed rapidly and found application in many fields, including natural language processing. In the field of aspect-level sentiment analysis, deep learning methods can also greatly improve the performance of models. However, previous studies did not take into account the relationship between user feature extraction and contextual terms. To address this issue, we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method. To be specific, we design user comment feature extraction (UCFE) to distill salient features from users’ historical comments and transform them into representative user feature vectors. Then, the aspect-sentence graph convolutional neural network (ASGCN) is used to incorporate innovative techniques for calculating adjacency matrices; meanwhile, ASGCN emphasizes capturing nuanced semantics within relationships among aspect words and syntactic dependency types. Afterward, three embedding methods are devised to embed the user feature vector into the ASGCN model. The empirical validations verify the effectiveness of these models, consistently surpassing conventional benchmarks and reaffirming the indispensable role of deep learning in advancing sentiment analysis methodologies.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Sentiment Analysis and Opinion Mining · Advanced Text Analysis Techniques · Topic Modeling
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (40)
Show source-provided references
1. Deep learning10.1038/nature14539 ↗
2. A hybrid attention based deep neural network for simultaneous multi-sensor pruning and human activity recognition10.1109/JIOT.2022.3196170 ↗
3. Automated pneumonia detection on chest X-ray images: A deep learning approach with different optimizers and transfer learning architectures10.1016/j.measurement.2021.109953 ↗
4. Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks10.1145/3385414 ↗
5. Sentiment analysis and opinion mining
6. A weakly supervised wordnet-guided deep learning approach to extracting aspect terms from online comments10.1145/3399630 ↗
7. A comprehensive survey on sentiment analysis: Approaches, challenges and trends10.1016/j.knosys.2021.107134 ↗
8. Adaptive learning method of Chinese domain sentiment dictionary
9. Employing BERT-DCNN with sentic knowledge base for social media sentiment analysis
10. A hybrid unsupervised method for aspect term and opinion target extraction10.1016/j.knosys.2018.01.019 ↗
11. DCU: Aspect-based polarity classification for SemEval Task 4
12. Feature selection and ensemble construction: A two-step method for aspect based sentiment analysis10.1016/j.knosys.2017.03.020 ↗
13. Joint inference for aspect-level sentiment analysis by deep neural networks and linguistic hints
14. RecogNet-LSTM+CNN: A hybrid network with attention mechanism for aspect categorization and sentiment classification10.1007/s10844-021-00692-3 ↗
15. ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis10.1016/j.future.2020.08.005 ↗
16. UserAdapter: Few-shot user learning in sentiment analysis
17. UserIdentifier: Implicit user representations for simple and effective personalized sentiment analysis
18. Cross-city analysis of location-based sentiment in user-generated text
19. A. Mahmoudi, “Identifying biased users in online social networks to enhance the accuracy of sentiment analysis: A user behavior-based approach,” arXiv:2105.05950v1, 2021.
20. A. Mahmoudi, V. W. Yeung, and W. K. Eric, “User behavior discovery in the COVID-19 era through the sentiment analysis of user tweet texts,” arXiv:2104.08867v1, 2021.
21. Aspect-based sentiment classification with aspect-specific graph convolutional networks
22. Improving aspect-based sentiment analysis with gated graph convolutional networks and syntax-based regulation
23. Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification10.1016/j.knosys.2019.105443 ↗
24. Relational graph attention network for aspect-based sentiment analysis
25. Aspect-based sentiment analysis with type-aware graph convolutional networks and layer ensemble
26. Dual graph convolutional networks for aspect-based sentiment analysis
27. A architecture-enhanced graph convolutional network for sentiment analysis
28. Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks
29. User modeling with neural network for comment rating prediction
30. Improving comment representations with user attention and product attention for sentiment classification
31. Rethinking attribute representation and injection for sentiment classification
32. Neural review rating prediction with user and product memory
33. Syntax-aware aspect level sentiment classification with graph attention networks
34. ARSA: A sentiment-aware model for predicting sales performance using blogs
35. BERT: Pre-training of deep bidirectional transformers for language understanding
36. Attentional encoder network for targeted sentiment classification
37. Selective attention based graph convolutional networks for aspect-level sentiment classification
38. Graph ensemble learning over multiple dependency trees for aspect-level sentiment classification
39. Jointly learning aspect-focused and inter-aspect relations with graph convolutional networks for aspect sentiment analysis
40. A cognitive brain model for multimodal sentiment analysis based on attention neural networks10.1016/j.neucom.2020.10.021 ↗