DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning

2018-10-01 · WS 2018 10 · Yasas Senarath, Uthayasanker Thayasivam

This paper describes an approach to solve implicit emotion classification with the use of pre-trained word embedding models to train multiple neural networks. The system described in this paper is composed of a sequential combination of Long Short-Term Memory and Convolutional Neural Network for feature extraction and Feedforward Neural Network for classification. In this paper, we successfully show that features extracted using multiple pre-trained embeddings can be used to improve the overall performance of the system with Emoji being one of the significant features. The evaluations show that our approach outperforms the baseline system by more than 8{\%} without using any external corpus or lexicon. This approach is ranked 8th in Implicit Emotion Shared Task (IEST) at WASSA-2018.

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Code (1)

ysenarath/opinion-lab

Tasks

Classification Emotion Classification General Classification Opinion Mining Sentiment Analysis Text Classification

Sentylic at IEST 2018: Gated Recurrent Neural Network and Capsule Network Based Approach for Implicit Emotion Detection

2018-09-05 · WS 2018 10 · Prabod Rathnayaka, Supun Abeysinghe, Chamod Samarajeewa, Isura Manchanayake 외

In this paper, we present the system we have used for the Implicit WASSA 2018 Implicit Emotion Shared Task. The task is to predict the emotion of a tweet of which the explicit mentions of emotion terms have been removed.…

Word Embeddings

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In this paper we present our approach to tackle the Implicit Emotion Shared Task (IEST) organized as part of WASSA 2018 at EMNLP 2018. Given a tweet, from which a certain word has been removed, we are asked to predict th…

Emotion Classification General Classification Sentiment Analysis Transfer Learning+1

NL-FIIT at IEST-2018: Emotion Recognition utilizing Neural Networks and Multi-level Preprocessing

2018-10-01 · WS 2018 10 · Samuel Pecar, Michal Farkas, Marian Simko, Peter Lacko 외

In this paper, we present neural models submitted to Shared Task on Implicit Emotion Recognition, organized as part of WASSA 2018. We propose a Bi-LSTM architecture with regularization through dropout and Gaussian noise.…

Emotion Recognition Sentence Sentence Embeddings Sentiment Analysis+1

USI-IR at IEST 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection

2018-10-01 · WS 2018 10 · Esteban R{\'\i}ssola, Anastasia Giachanou, Fabio Crestani

This paper describes the participation of USI-IR in WASSA 2018 Implicit Emotion Shared Task. We propose a relevance feedback approach employing a sequential model (biLSTM) and word embeddings derived from a large collect…

Emotion Recognition General Classification Sentiment Analysis Word Embeddings

EmoNLP at IEST 2018: An Ensemble of Deep Learning Models and Gradient Boosting Regression Tree for Implicit Emotion Prediction in Tweets

2018-10-01 · WS 2018 10 · Man Liu

This paper describes our system submitted to IEST 2018, a shared task (Klinger et al., 2018) to predict the emotion types. Six emotion types are involved: anger, joy, fear, surprise, disgust and sad. We perform three dif…

regression Word Embeddings