# Dialog speech sentiment classification for imbalanced datasets

arXiv: https://arxiv.org/abs/2109.07228 · HTML: https://mlsanity.com/paper/2109.07228/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines](https://mlsanity.com/paper/1803.07427/) (2018)
- [Speech Sentiment Analysis via Pre-trained Features from End-to-end ASR Models](https://mlsanity.com/paper/1911.09762/) (2019)
- [Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment](https://mlsanity.com/paper/1805.08660/) (2018)
- [Deep neural networks for emotion recognition combining audio and transcripts](https://mlsanity.com/paper/1911.00432/) (2019)

## Abstract (arXiv)

Speech is the most common way humans express their feelings, and sentiment analysis is the use of tools such as natural language processing and computational algorithms to identify the polarity of these feelings. Even though this field has seen tremendous advancements in the last two decades, the task of effectively detecting under represented sentiments in different kinds of datasets is still a challenging task. In this paper, we use single and bi-modal analysis of short dialog utterances and gain insights on the main factors that aid in sentiment detection, particularly in the underrepresented classes, in datasets with and without inherent sentiment component. Furthermore, we propose an architecture which uses a learning rate scheduler and different monitoring criteria and provides state-of-the-art results for the SWITCHBOARD imbalanced sentiment dataset.