scholarly journals EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks

2017 ◽  
Author(s):  
Muhammad Abdul-Mageed ◽  
Lyle Ungar
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Emotion Detection ◽  
Fine Grained

scholarly journals Hybrid Approaches to Fine-Grained Emotion Detection in Social Media Data

2020 ◽  
Vol 34 (10) ◽  
pp. 13732-13733
Author(s):  
Annika Marie Schoene
Keyword(s):  
Social Media ◽  
Recurrent Neural Networks ◽  
Emotion Detection ◽  
Problem Statement ◽  
Future Directions ◽  
Social Media Data ◽  
Fine Grained ◽  
Hybrid Approaches ◽  
Research Plan ◽  
Media Data

This paper states the challenges in fine-grained target-dependent Sentiment Analysis for social media data using recurrent neural networks. First, the problem statement is outlined and an overview of related work in the area is given. Then a summary of progress and results achieved to date and a research plan and future directions of this work are given.

scholarly journals DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

2019 ◽  
Vol 33 ◽  
pp. 6818-6825 ◽  
Author(s):  
Navonil Majumder ◽  
Soujanya Poria ◽  
Devamanyu Hazarika ◽  
Rada Mihalcea ◽  
Alexander Gelbukh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Recurrent Neural Networks ◽  
Opinion Mining ◽  
State Of The Art ◽  
Emotion Detection ◽  
Emotion Classification ◽  
Argumentation Mining ◽  
Consumer Feedback ◽  
The Individual

Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, and so on. Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance. In this paper, we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification. Our model outperforms the state-of-the-art by a significant margin on two different datasets.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

Sign in / Sign up

Export Citation Format

Share Document