# Deep Learning Model for Interpretability and Explainability of Aspect-Level Sentiment Analysis Based on Social Media

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/deep-learning-model-for-interpretability-and-explainability-of-aspect-level/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Nikhil Singh,Sanjay Agal,Thippa Reddy Gadekallu,Mohammad Shabaz,Ismail Keshta,Latika Jindal,Mukesh Soni,Haewon Byeon,Pavitar Parkash Singh |
| Citations | 11 |
| DOI | 10.1109/tcss.2023.3347664 |
| Fields | Computer Science |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W4391128505 |
| Type | article |
| Year | 2024 |

## Paper authors

- [Sanjay Agal](https://scholariq.org/researchers/sanjay-agal/)

## Paper primary topic

- [Advanced Text Analysis Techniques](https://scholariq.org/topics/advanced-text-analysis-techniques/)

## Paper topics

- [Advanced Text Analysis Techniques](https://scholariq.org/topics/advanced-text-analysis-techniques/)
- [Sentiment Analysis and Opinion Mining](https://scholariq.org/topics/sentiment-analysis-and-opinion-mining/)
- [Technology and Data Analysis](https://scholariq.org/topics/technology-and-data-analysis/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.