294 research outputs found
Detecting emotions using a combination of bidirectional encoder representations from transformers embedding and bidirectional long short-term memory
One of the most difficult topics in natural language understanding (NLU) is emotion detection in text because human emotions are difficult to understand without knowing facial expressions. Because the structure of Indonesian differs from other languages, this study focuses on emotion detection in Indonesian text. The nine experimental scenarios of this study incorporate word embedding (bidirectional encoder representations from transformers (BERT), Word2Vec, and GloVe) and emotion detection models (bidirectional long short-term memory (BiLSTM), LSTM, and convolutional neural network (CNN)). With values of 88.28%, 88.42%, and 89.20% for Commuter Line, Transjakarta, and Commuter Line+Transjakarta, respectively, BERT-BiLSTM generates the highest accuracy on the data. In general, BiLSTM produces the highest accuracy, followed by LSTM, and finally CNN. When it came to word embedding, BERT embedding outperformed Word2Vec and GloVe. In addition, the BERT-BiLSTM model generates the highest precision, recall, and F1-measure values in each data scenario when compared to other models. According to the results of this study, BERT-BiLSTM can enhance the performance of the classification model when compared to previous studies that only used BERT or BiLSTM for emotion detection in Indonesian texts
Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs
To make machines better understand sentiments, research needs to move from
polarity identification to understanding the reasons that underlie the
expression of sentiment. Categorizing the goals or needs of humans is one way
to explain the expression of sentiment in text. Humans are good at
understanding situations described in natural language and can easily connect
them to the character's psychological needs using commonsense knowledge. We
present a novel method to extract, rank, filter and select multi-hop relation
paths from a commonsense knowledge resource to interpret the expression of
sentiment in terms of their underlying human needs. We efficiently integrate
the acquired knowledge paths in a neural model that interfaces context
representations with knowledge using a gated attention mechanism. We assess the
model's performance on a recently published dataset for categorizing human
needs. Selectively integrating knowledge paths boosts performance and
establishes a new state-of-the-art. Our model offers interpretability through
the learned attention map over commonsense knowledge paths. Human evaluation
highlights the relevance of the encoded knowledge
Recognizing Emotions in Short Texts
Tese de mestrado, Ciência Cognitiva, Universidade de Lisboa, Faculdade de Ciências, 2022O reconhecimento automático de emoções em texto é uma tarefa que mobiliza as áreas de processamento
de linguagem natural e de computação afetiva, para as quais se pode contar com o especial contributo
de disciplinas da Ciência Cognitiva como Inteligência Artificial e Ciência da Computação, Linguística
e Psicologia. Visa, sobretudo, a deteção e interpretação de emoções humanas através da sua expressão
na forma escrita por sistemas computacionais.
A interação entre processos afetivos e cognitivos, o papel essencial que as emoções
desempenham nas interações interpessoais e a crescente utilização de comunicação escrita online nos
dias de hoje fazem com que o reconhecimento de emoções de forma automática seja cada vez mais
importante, nomeadamente em áreas como saúde mental, interação pessoa-computador, ciência política
ou marketing.
A língua inglesa tem sido o maior alvo de estudo no que diz respeito ao reconhecimento de
emoções em textos, sendo que ainda existe pouco trabalho desenvolvido para a língua portuguesa.
Assim, existe uma necessidade em expandir o trabalho feito para a língua inglesa para o português.
Esta dissertação tem como objetivo a comparação de dois métodos distintos de aprendizagem
profunda resultantes dos avanços na área de Inteligência Artificial para detetar e classificar de forma
automática estados emocionais discretos em textos escritos em língua portuguesa.
Para tal, a abordagem de classificação de Polignano et al. (2019) baseada em redes de
aprendizagem profunda como Long Short-Term Memory bidirecionais e redes convolucionais mediadas
por um mecanismo de atenção será replicada para a língua inglesa e será reproduzida para a língua
portuguesa. Para a língua inglesa, será utilizado o conjunto de dados da tarefa 1 do SemEval-2018
(Mohammad et al., 2018) tal como na experiência original, que considera quatro emoções discretas:
raiva, medo, alegria e tristeza. Para a língua portuguesa, tendo em consideração a falta de conjuntos de
dados disponíveis anotados relativamente a emoções, será efetuada uma recolha de dados a partir da
rede social Twitter recorrendo a hashtags com conteúdo associado a uma emoção específica para
determinar a emoção subjacente ao texto de entre as mesmas quatro emoções presentes no conjunto de
dados da língua inglesa que será utilizado. De acordo com experiências realizadas por Mohammad &
Kiritchenko (2015), este método de recolha de dados é consistente com a anotação de juízes humanos
treinados.
Tendo em conta a rápida e contínua evolução dos métodos de aprendizagem profunda para o
processamento de linguagem natural e o estado da arte estabelecido por métodos recentes em tarefas
desta área tal como o modelo pré-treinado BERT (Bidirectional Encoder Representations from
Tranformers) (Devlin et al., 2019), será também aplicada esta abordagem para a tarefa de
reconhecimento de emoções para as duas línguas em questão, utilizando os mesmos conjuntos de dados
das experiências anteriores.
Enquanto a abordagem de Polignano et al. teve um melhor desempenho nas experiências que
realizámos com dados em inglês, com diferenças de F1-score de 0.02, o melhor resultado obtido nas
experiências com dados na língua portuguesa foi com o modelo BERT, obtendo um resultado máximo
de F1-score de 0.6124.Automatic emotion recognition from text is a task that mobilizes the areas of natural language processing
and affective computing counting with the special contribution of Cognitive Science subjects such as
Artificial Intelligence and Computer Science, Linguistics and Psychology. It aims at the detection and
interpretation of human emotions expressed in the written form by computational systems.
The interaction of affective and cognitive processes, the essential role that emotions play in
interpersonal interactions and the currently increasing use of written communication online make
automatic emotion recognition progressively important, namely in areas such as mental healthcare,
human-computer interaction, political science, or marketing.
The English language has been the main target of studies in emotion recognition in text and the
work developed for the Portuguese language is still scarce. Thus, there is a need to expand the work
developed for English to Portuguese.
The goal of this dissertation is to present and compare two distinct deep learning methods
resulting from the advances in Artificial Intelligence to automatically detect and classify discrete
emotional states in texts written in Portuguese.
For this, the classification approach of Polignano et al. (2019) based on deep learning networks
such as bidirectional Long Short-Term Memory and convolutional networks mediated by a self-attention
level will be replicated for English and it will be reproduced for Portuguese. For English, the
SemEval-2018 task 1 dataset (Mohammad et al., 2018) will be used, as in the original experience, and
it considers four discrete emotions: anger, fear, joy, and sadness. For Portuguese, considering the lack
of available emotionally annotated datasets, data will be collected from the social network Twitter using
hashtags associated to a specific emotional content to determine the underlying emotion of the text from
the same four emotions present in the English dataset. According to experiments carried out by
Mohammad & Kiritchenko (2015), this method of data collection is consistent with the annotation of
trained human judges.
Considering the fast and continuous evolution of deep learning methods for natural language
processing and the state-of-the-art results achieved by recent methods in tasks in this area such as the
pre-trained language model BERT (Bidirectional Encoder Representations from Transformers)
(Devlin et al., 2019), this approach will also be applied to the task of emotion recognition for both
languages using the same datasets from the previous experiments. It is expected to draw conclusions
about the adequacy of these two presented approaches in emotion recognition and to contribute to the
state of the art in this task for the Portuguese language.
While the approach of Polignano et al. had a better performance in the experiments with English
data with a difference in F1 scores of 0.02, for Portuguese we obtained the best result with BERT having
a maximum F1 score of 0.6124
Document-level sentiment analysis of email data
Sisi Liu investigated machine learning methods for Email document sentiment analysis. She developed a systematic framework that has been qualitatively and quantitatively proved to be effective and efficient in identifying sentiment from massive amount of Email data. Analytical results obtained from the document-level Email sentiment analysis framework are beneficial for better decision making in various business settings
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