18,585 research outputs found
Emotion Correlation Mining Through Deep Learning Models on Natural Language Text
Emotion analysis has been attracting researchers' attention. Most previous
works in the artificial intelligence field focus on recognizing emotion rather
than mining the reason why emotions are not or wrongly recognized. Correlation
among emotions contributes to the failure of emotion recognition. In this
paper, we try to fill the gap between emotion recognition and emotion
correlation mining through natural language text from web news. Correlation
among emotions, expressed as the confusion and evolution of emotion, is
primarily caused by human emotion cognitive bias. To mine emotion correlation
from emotion recognition through text, three kinds of features and two deep
neural network models are presented. The emotion confusion law is extracted
through orthogonal basis. The emotion evolution law is evaluated from three
perspectives, one-step shift, limited-step shifts, and shortest path transfer.
The method is validated using three datasets-the titles, the bodies, and the
comments of news articles, covering both objective and subjective texts in
varying lengths (long and short). The experimental results show that, in
subjective comments, emotions are easily mistaken as anger. Comments tend to
arouse emotion circulations of love-anger and sadness-anger. In objective news,
it is easy to recognize text emotion as love and cause fear-joy circulation.
That means, journalists may try to attract attention using fear and joy words
but arouse the emotion love instead; After news release, netizens generate
emotional comments to express their intense emotions, i.e., anger, sadness, and
love. These findings could provide insights for applications regarding
affective interaction such as network public sentiment, social media
communication, and human-computer interaction
A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction
In this paper, through multi-task ensemble framework we address three
problems of emotion and sentiment analysis i.e. "emotion classification &
intensity", "valence, arousal & dominance for emotion" and "valence & arousal}
for sentiment". The underlying problems cover two granularities (i.e.
coarse-grained and fine-grained) and a diverse range of domains (i.e. tweets,
Facebook posts, news headlines, blogs, letters etc.). The ensemble model aims
to leverage the learned representations of three deep learning models (i.e.
CNN, LSTM and GRU) and a hand-crafted feature representation for the
predictions. Experimental results on the benchmark datasets show the efficacy
of our proposed multi-task ensemble frameworks. We obtain the performance
improvement of 2-3 points on an average over single-task systems for most of
the problems and domains
Deep Learning for Sentiment Analysis : A Survey
Deep learning has emerged as a powerful machine learning technique that
learns multiple layers of representations or features of the data and produces
state-of-the-art prediction results. Along with the success of deep learning in
many other application domains, deep learning is also popularly used in
sentiment analysis in recent years. This paper first gives an overview of deep
learning and then provides a comprehensive survey of its current applications
in sentiment analysis.Comment: 34 pages, 9 figures, 2 table
Estimation of Inter-Sentiment Correlations Employing Deep Neural Network Models
This paper focuses on sentiment mining and sentiment correlation analysis of
web events. Although neural network models have contributed a lot to mining
text information, little attention is paid to analysis of the inter-sentiment
correlations. This paper fills the gap between sentiment calculation and
inter-sentiment correlations. In this paper, the social emotion is divided into
six categories: love, joy, anger, sadness, fear, and surprise. Two deep neural
network models are presented for sentiment calculation. Three datasets - the
titles, the bodies, the comments of news articles - are collected, covering
both objective and subjective texts in varying lengths (long and short). From
each dataset, three kinds of features are extracted: explicit expression,
implicit expression, and alphabet characters. The performance of the two models
are analyzed, with respect to each of the three kinds of the features. There is
controversial phenomenon on the interpretation of anger (fn) and love (gd). In
subjective text, other emotions are easily to be considered as anger. By
contrast, in objective news bodies and titles, it is easy to regard text as
caused love (gd). It means, journalist may want to arouse emotion love by
writing news, but cause anger after the news is published. This result reflects
the sentiment complexity and unpredictability
Semi-supervised emotion lexicon expansion with label propagation and specialized word embeddings
There exist two main approaches to automatically extract affective
orientation: lexicon-based and corpus-based. In this work, we argue that these
two methods are compatible and show that combining them can improve the
accuracy of emotion classifiers. In particular, we introduce a novel variant of
the Label Propagation algorithm that is tailored to distributed word
representations, we apply batch gradient descent to accelerate the optimization
of label propagation and to make the optimization feasible for large graphs,
and we propose a reproducible method for emotion lexicon expansion. We conclude
that label propagation can expand an emotion lexicon in a meaningful way and
that the expanded emotion lexicon can be leveraged to improve the accuracy of
an emotion classifier
EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets
This paper describes our system that has been used in Task1 Affect in Tweets.
We combine two different approaches. The first one called N-Stream ConvNets,
which is a deep learning approach where the second one is XGboost regresseor
based on a set of embedding and lexicons based features. Our system was
evaluated on the testing sets of the tasks outperforming all other approaches
for the Arabic version of valence intensity regression task and valence ordinal
classification task
Emotion Detection in Text: a Review
In recent years, emotion detection in text has become more popular due to its
vast potential applications in marketing, political science, psychology,
human-computer interaction, artificial intelligence, etc. Access to a huge
amount of textual data, especially opinionated and self-expression text also
played a special role to bring attention to this field. In this paper, we
review the work that has been done in identifying emotion expressions in text
and argue that although many techniques, methodologies, and models have been
created to detect emotion in text, there are various reasons that make these
methods insufficient. Although, there is an essential need to improve the
design and architecture of current systems, factors such as the complexity of
human emotions, and the use of implicit and metaphorical language in expressing
it, lead us to think that just re-purposing standard methodologies will not be
enough to capture these complexities, and it is important to pay attention to
the linguistic intricacies of emotion expression
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research
Sentiment analysis as a field has come a long way since it was first
introduced as a task nearly 20 years ago. It has widespread commercial
applications in various domains like marketing, risk management, market
research, and politics, to name a few. Given its saturation in specific
subtasks -- such as sentiment polarity classification -- and datasets, there is
an underlying perception that this field has reached its maturity. In this
article, we discuss this perception by pointing out the shortcomings and
under-explored, yet key aspects of this field that are necessary to attain true
sentiment understanding. We analyze the significant leaps responsible for its
current relevance. Further, we attempt to chart a possible course for this
field that covers many overlooked and unanswered questions.Comment: Published in the IEEE Transactions on Affective Computing (TAFFC
Deep Cross-Modal Correlation Learning for Audio and Lyrics in Music Retrieval
Little research focuses on cross-modal correlation learning where temporal
structures of different data modalities such as audio and lyrics are taken into
account. Stemming from the characteristic of temporal structures of music in
nature, we are motivated to learn the deep sequential correlation between audio
and lyrics. In this work, we propose a deep cross-modal correlation learning
architecture involving two-branch deep neural networks for audio modality and
text modality (lyrics). Different modality data are converted to the same
canonical space where inter modal canonical correlation analysis is utilized as
an objective function to calculate the similarity of temporal structures. This
is the first study on understanding the correlation between language and music
audio through deep architectures for learning the paired temporal correlation
of audio and lyrics. Pre-trained Doc2vec model followed by fully-connected
layers (fully-connected deep neural network) is used to represent lyrics. Two
significant contributions are made in the audio branch, as follows: i)
pre-trained CNN followed by fully-connected layers is investigated for
representing music audio. ii) We further suggest an end-to-end architecture
that simultaneously trains convolutional layers and fully-connected layers to
better learn temporal structures of music audio. Particularly, our end-to-end
deep architecture contains two properties: simultaneously implementing feature
learning and cross-modal correlation learning, and learning joint
representation by considering temporal structures. Experimental results, using
audio to retrieve lyrics or using lyrics to retrieve audio, verify the
effectiveness of the proposed deep correlation learning architectures in
cross-modal music retrieval
Tensor Fusion Network for Multimodal Sentiment Analysis
Multimodal sentiment analysis is an increasingly popular research area, which
extends the conventional language-based definition of sentiment analysis to a
multimodal setup where other relevant modalities accompany language. In this
paper, we pose the problem of multimodal sentiment analysis as modeling
intra-modality and inter-modality dynamics. We introduce a novel model, termed
Tensor Fusion Network, which learns both such dynamics end-to-end. The proposed
approach is tailored for the volatile nature of spoken language in online
videos as well as accompanying gestures and voice. In the experiments, our
model outperforms state-of-the-art approaches for both multimodal and unimodal
sentiment analysis.Comment: Accepted as full paper in EMNLP 201
- …