# A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis

arXiv: https://arxiv.org/abs/2103.02636 · HTML: https://mlsanity.com/paper/2103.02636/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 69 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [ParsBERT: Transformer-based Model for Persian Language Understanding](https://mlsanity.com/paper/2005.12515/) (2020)
- [Lip-Reading Driven Deep Learning Approach for Speech Enhancement](https://mlsanity.com/paper/1808.00046/) (2018)
- [Benchmarking Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.09538/) (2017)
- [A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks](https://mlsanity.com/paper/1909.13568/) (2019)
- [Exploiting Deep Learning for Persian Sentiment Analysis](https://mlsanity.com/paper/1808.05077/) (2018)
- [CochleaNet: A Robust Language-independent Audio-Visual Model for Speech Enhancement](https://mlsanity.com/paper/1909.10407/) (2019)
- [A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management](https://mlsanity.com/paper/1909.10353/) (2019)
- [AV Speech Enhancement Challenge using a Real Noisy Corpus](https://mlsanity.com/paper/1910.00424/) (2019)

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

- [A Survey on sentiment analysis in Persian: A Comprehensive System Perspective Covering Challenges and Advances in Resources, and Methods](https://mlsanity.com/paper/2104.14751/) (2021)
- [Large Multimodal Models for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2502.05568/) (2025)
- [Multimodal Emotion Recognition by Fusing Video Semantic in MOOC Learning Scenarios](https://mlsanity.com/paper/2404.07484/) (2024)

## Abstract (arXiv)

Most recent works on sentiment analysis have exploited the text modality. However, millions of hours of video recordings posted on social media platforms everyday hold vital unstructured information that can be exploited to more effectively gauge public perception. Multimodal sentiment analysis offers an innovative solution to computationally understand and harvest sentiments from videos by contextually exploiting audio, visual and textual cues. In this paper, we, firstly, present a first of its kind Persian multimodal dataset comprising more than 800 utterances, as a benchmark resource for researchers to evaluate multimodal sentiment analysis approaches in Persian language. Secondly, we present a novel context-aware multimodal sentiment analysis framework, that simultaneously exploits acoustic, visual and textual cues to more accurately determine the expressed sentiment. We employ both decision-level (late) and feature-level (early) fusion methods to integrate affective cross-modal information. Experimental results demonstrate that the contextual integration of multimodal features such as textual, acoustic and visual features deliver better performance (91.39%) compared to unimodal features (89.24%).