DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method

2017-09-01 · WS 2017 9 · Song Jiang, Xiaotian Han

In this paper, we present a novel ensemble learning architecture for emotion intensity analysis, particularly a novel framework of ensemble method. The ensemble method has two stages and each stage includes several single machine learning models. In stage1, we employ both linear and nonlinear regression models to obtain a more diverse emotion intensity representation. In stage2, we use two regression models including linear regression and XGBoost. The result of stage1 serves as the input of stage2, so the two different type models (linear and non-linear) in stage2 can describe the input in two opposite aspects. We also added a method for analyzing and splitting multi-words hashtags and appending them to the emotion intensity corpus before feeding it to our model. Our model achieves 0.571 Pearson-measure for the average of four emotions.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Emotion Classification Emotion Recognition Ensemble Learning regression

Methods 이 논문이 사용한 방법론

Linear Regression Linear Regression is a method for modelling a relationship between a dependent variable and independent variables. These models can be fit with numerous approaches. The most…

UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings

2017-09-01 · WS 2017 9 · Vineet John, Olga Vechtomova

This paper describes the UWaterloo affect prediction system developed for EmoInt-2017. We delve into our feature selection approach for affect intensity, affect presence, sentiment intensity and sentiment presence lexica…

Emotion Classification Ensemble Learning feature selection regression+1

Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets

2017-09-01 · WS 2017 9 · Pranav Goel, Devang Kulshreshtha, Prayas Jain, Kaushal Kumar Shukla

The paper describes the best performing system for EmoInt - a shared task to predict the intensity of emotions in tweets. Intensity is a real valued score, between 0 and 1. The emotions are classified as - anger, fear, j…

Sentiment Analysis

GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection

2017-09-01 · WS 2017 9 · Egor Lakomkin, Ch Bothe, rakant, Stefan Wermter

The WASSA 2017 EmoInt shared task has the goal to predict emotion intensity values of tweet messages. Given the text of a tweet and its emotion category (anger, joy, fear, and sadness), the participants were asked to bui…

Language Modeling Language Modelling Machine Translation Sentiment Analysis+1

GradAscent at EmoInt-2017: Character- and Word-Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection

2018-03-30 · Egor Lakomkin, Chandrakant Bothe, Stefan Wermter

Seernet at EmoInt-2017: Tweet Emotion Intensity Estimator

2017-08-21 · WS 2017 9 · Venkatesh Duppada, Sushant Hiray

The paper describes experiments on estimating emotion intensity in tweets using a generalized regressor system. The system combines lexical, syntactic and pre-trained word embedding features, trains them on general regre…