# A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications

arXiv: https://arxiv.org/abs/2311.11250 · HTML: https://mlsanity.com/paper/2311.11250/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 124 references that are in the map.
- First posted on arXiv 19 Nov 2023.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Multi-attention Recurrent Network for Human Communication Comprehension](https://mlsanity.com/paper/1802.00923/) (2018)
- [A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1610.08815/) (2016)
- [Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages](https://mlsanity.com/paper/1812.10411/) (2018)
- [The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements](https://mlsanity.com/paper/2101.06053/) (2021)
- [Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks](https://mlsanity.com/paper/1808.09238/) (2018)
- [From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction](https://mlsanity.com/paper/1604.03489/) (2016)
- [Visual Sentiment Prediction with Deep Convolutional Neural Networks](https://mlsanity.com/paper/1411.5731/) (2014)
- [Incorporating End-to-End Speech Recognition Models for Sentiment Analysis](https://mlsanity.com/paper/1902.11245/) (2019)

## Built on by (most cited first, 3 of 3)

- [Turning Generative Models Degenerate: The Power of Data Poisoning Attacks](https://mlsanity.com/paper/2407.12281/) (2024)
- [Performance Evaluation of Emotion Classification in Japanese Using RoBERTa and DeBERTa](https://mlsanity.com/paper/2505.00013/) (2025)
- [Named Entity Recognition for the Kurdish Sorani Language: Dataset Creation and Comparative Analysis](https://mlsanity.com/paper/2511.22315/) (2025)

## Abstract (arXiv)

Sentiment analysis (SA) is an emerging field in text mining. It is the process of computationally identifying and categorizing opinions expressed in a piece of text over different social media platforms. Social media plays an essential role in knowing the customer mindset towards a product, services, and the latest market trends. Most organizations depend on the customer's response and feedback to upgrade their offered products and services. SA or opinion mining seems to be a promising research area for various domains. It plays a vital role in analyzing big data generated daily in structured and unstructured formats over the internet. This survey paper defines sentiment and its recent research and development in different domains, including voice, images, videos, and text. The challenges and opportunities of sentiment analysis are also discussed in the paper. keywordsSentiment Analysis, Machine Learning, Lexicon-based approach, Deep Learning, Natural Language Processing