# BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis

arXiv: https://arxiv.org/abs/2006.00492 · HTML: https://mlsanity.com/paper/2006.00492/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 28 of 28)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa](https://mlsanity.com/paper/2108.12009/) (2021)
- [Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection](https://mlsanity.com/paper/2106.01071/) (2021)
- [Revisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusion](https://mlsanity.com/paper/2404.17858/) (2024)
- [A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning](https://mlsanity.com/paper/2312.05735/) (2023)
- [Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations](https://mlsanity.com/paper/2407.00119/) (2024)
- [Discovering Emotion and Reasoning its Flip in Multi-Party Conversations using Masked Memory Network and Transformer](https://mlsanity.com/paper/2103.12360/) (2021)
- [Deep Emotion Recognition in Textual Conversations: A Survey](https://mlsanity.com/paper/2211.09172/) (2022)
- [M2R2: Missing-Modality Robust emotion Recognition framework with iterative data augmentation](https://mlsanity.com/paper/2205.02524/) (2022)
- [EDSA-Ensemble: an Event Detection Sentiment Analysis Ensemble Architecture](https://mlsanity.com/paper/2301.12805/) (2023)
- [Emotion Dynamics Modeling via BERT](https://mlsanity.com/paper/2104.07252/) (2021)
- [Graph Routing between Capsules](https://mlsanity.com/paper/2106.11531/) (2021)
- [SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)](https://mlsanity.com/paper/2402.18944/) (2024)
- [Emotion Flip Reasoning in Multiparty Conversations](https://mlsanity.com/paper/2306.13959/) (2023)
- [Multimodal Representations Learning Based on Mutual Information Maximization and Minimization and Identity Embedding for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2201.03969/) (2022)
- [Embedding generation for text classification of Brazilian Portuguese user reviews: from bag-of-words to transformers](https://mlsanity.com/paper/2212.00587/) (2022)
- [EmoTwiCS: A Corpus for Modelling Emotion Trajectories in Dutch Customer Service Dialogues on Twitter](https://mlsanity.com/paper/2310.06536/) (2023)
- [From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues](https://mlsanity.com/paper/2310.13080/) (2023)
- [ESIHGNN: Event-State Interactions Infused Heterogeneous Graph Neural Network for Conversational Emotion Recognition](https://mlsanity.com/paper/2405.03960/) (2024)
- [AdCOFE: Advanced Contextual Feature Extraction in Conversations for emotion classification](https://mlsanity.com/paper/2104.04517/) (2021)
- [A Dual-Stream Recurrence-Attention Network With Global-Local Awareness for Emotion Recognition in Textual Dialog](https://mlsanity.com/paper/2307.00449/) (2023)
- [Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network](https://mlsanity.com/paper/2402.02184/) (2024)
- [Bias Beneath the Tone: Empirical Characterisation of Tone Bias in LLM-Driven UX Systems](https://mlsanity.com/paper/2512.19950/) (2025)
- [Which Factors Predict the Chat Experience of a Natural Language Generation Dialogue Service?](https://mlsanity.com/paper/2304.10785/) (2023)
- [MasonTigers at SemEval-2024 Task 10: Emotion Discovery and Flip Reasoning in Conversation with Ensemble of Transformers and Prompting](https://mlsanity.com/paper/2407.00581/) (2024)
- [TED: Turn Emphasis with Dialogue Feature Attention for Emotion Recognition in Conversation](https://mlsanity.com/paper/2501.01123/) (2025)
- [A Customer Journey in the Land of Oz: Leveraging the Wizard of Oz Technique to Model Emotions in Customer Service Interactions](https://mlsanity.com/paper/2511.21909/) (2025)
- [Causal Emotion Recognition in Conversation: Context Saturation and Discourse-Marker Evidence](https://mlsanity.com/paper/2601.00181/) (2026)

## Abstract (arXiv)

Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e.g., sentiment analysis, recommender systems, and human-robot interaction. The main difference between conversational sentiment analysis and single sentence sentiment analysis is the existence of context information which may influence the sentiment of an utterance in a dialogue. How to effectively encode contextual information in dialogues, however, remains a challenge. Existing approaches employ complicated deep learning structures to distinguish different parties in a conversation and then model the context information. In this paper, we propose a fast, compact and parameter-efficient party-ignorant framework named bidirectional emotional recurrent unit for conversational sentiment analysis. In our system, a generalized neural tensor block followed by a two-channel classifier is designed to perform context compositionality and sentiment classification, respectively. Extensive experiments on three standard datasets demonstrate that our model outperforms the state of the art in most cases.