A New Feature Selection Scheme for Emotion Recognition from Text

Erenel, Zafer; Adegboye, Oluwatayomi Rereloluwa; Kusetoğulları, Hüseyin

doi:10.3390/app10155351

Cited by 18 publications

(8 citation statements)

References 55 publications

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“…For micro-expression recognition, Shaheen et al [18] proposed a framework for an emotion recognition system that treats emotions as generalized ideas abstracted from sentences by incorporating compositional, syntactic, and semantic analysis. Erenel et al [19] developed and compared a new feature selection approach for emotion classification to various feature reduction techniques, including chi-square, Gini-text, and delta. The proposed approach, known as the relevance score, was shown to improve emotion classification.…”

Section: Recent Workmentioning

confidence: 99%

Micro-Expression-Based Emotion Recognition Using Waterfall Atrous Spatial Pyramid Pooling Networks

Stofa

Zulkifley

Zainuri

2022

Sensors

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Understanding a person’s attitude or sentiment from their facial expressions has long been a straightforward task for humans. Numerous methods and techniques have been used to classify and interpret human emotions that are commonly communicated through facial expressions, with either macro- or micro-expressions. However, performing this task using computer-based techniques or algorithms has been proven to be extremely difficult, whereby it is a time-consuming task to annotate it manually. Compared to macro-expressions, micro-expressions manifest the real emotional cues of a human, which they try to suppress and hide. Different methods and algorithms for recognizing emotions using micro-expressions are examined in this research, and the results are presented in a comparative approach. The proposed technique is based on a multi-scale deep learning approach that aims to extract facial cues of various subjects under various conditions. Then, two popular multi-scale approaches are explored, Spatial Pyramid Pooling (SPP) and Atrous Spatial Pyramid Pooling (ASPP), which are then optimized to suit the purpose of emotion recognition using micro-expression cues. There are four new architectures introduced in this paper based on multi-layer multi-scale convolutional networks using both direct and waterfall network flows. The experimental results show that the ASPP module with waterfall network flow, which we coined as WASPP-Net, outperforms the state-of-the-art benchmark techniques with an accuracy of 80.5%. For future work, a high-resolution approach to multi-scale approaches can be explored to further improve the recognition performance.

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Section: Recent Workmentioning

confidence: 99%

Micro-Expression-Based Emotion Recognition Using Waterfall Atrous Spatial Pyramid Pooling Networks

Stofa

Zulkifley

Zainuri

2022

Sensors

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“…The gradient boost model reported the best F1-score when used with the hybrid sentiment-aware embeddings. In [35], a new feature selection scheme for emotion classification was proposed and compared to other feature reduction techniques, namely chi-square, Gini-text, and delta. The proposed scheme was called relevance score and was proved to improve the classification of emotions.…”

Section: Background and Related Workmentioning

confidence: 99%

The Evolution of Language Models Applied to Emotion Analysis of Arabic Tweets

Al-Twairesh

2021

Information

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The field of natural language processing (NLP) has witnessed a boom in language representation models with the introduction of pretrained language models that are trained on massive textual data then used to fine-tune downstream NLP tasks. In this paper, we aim to study the evolution of language representation models by analyzing their effect on an under-researched NLP task: emotion analysis; for a low-resource language: Arabic. Most of the studies in the field of affect analysis focused on sentiment analysis, i.e., classifying text into valence (positive, negative, neutral) while few studies go further to analyze the finer grained emotional states (happiness, sadness, anger, etc.). Emotion analysis is a text classification problem that is tackled using machine learning techniques. Different language representation models have been used as features for these machine learning models to learn from. In this paper, we perform an empirical study on the evolution of language models, from the traditional term frequency–inverse document frequency (TF–IDF) to the more sophisticated word embedding word2vec, and finally the recent state-of-the-art pretrained language model, bidirectional encoder representations from transformers (BERT). We observe and analyze how the performance increases as we change the language model. We also investigate different BERT models for Arabic. We find that the best performance is achieved with the ArabicBERT large model, which is a BERT model trained on a large dataset of Arabic text. The increase in F1-score was significant +7–21%.

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“…Gharibshah et al also used LSTM to create a deep learning framework, that could predict users' interest and display recommended advertisements, when they are browsing the website [14] . In addition, deep learning techniques have been used with various types of data other than clicking log data—e.g., face [15] , [16] , eye gaze [17] , gesture [18] , text [19] , [20] , [21] , [22] —to assist in understanding customers and improving customer satisfaction. However, a deep learning technique also requires a large labeled dataset to train a generalized model, but, again, collecting a large labeled dataset is expensive and laborious.…”

Section: Related Workmentioning

confidence: 99%

Identifying SME customers from click feedback on mobile banking apps: Supervised and semi-supervised approaches

Tungjitnob

Pasupa

Suntisrivaraporn

2021

Heliyon

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Nowadays, the banking industry has moved from traditional branch services into mobile banking applications or apps. Using customer segmentation, banks can obtain more insights and better understand their customers' lifestyle and their behavior. In this work, we described a method to classify mobile app user click behavior into two groups, i.e. SME and Non-SME users. This task enabled the bank to identify anonymous users and offer them the right services and products. We extracted hand-crafted features from click log data and evaluated them with the Extreme Gradient Boosting algorithm (XGBoost). We also converted these logs into images, which captured temporal information. These image representations reduced the need for feature engineering, were easier to visualize and trained with a Convolutional Neural Network (CNN). We used ResNet-18 with the image dataset and achieved 71.69% accuracy on average, which outperformed XGBoost, which only achieved 61.70% accuracy. We also evaluated a semi-supervised learning model with our converted image data. Our semi-supervised method achieved 73.12% accuracy, using just half of the labeled images, combined with unlabeled images. Our method showed that these converted images were able to train with a semi-supervised algorithm that performed better than CNN with fewer labeled images. Our work also led to a better understanding of mobile banking user behavior and a novel way of developing a customer segmentation classifier.

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A New Feature Selection Scheme for Emotion Recognition from Text

Cited by 18 publications

References 55 publications

Micro-Expression-Based Emotion Recognition Using Waterfall Atrous Spatial Pyramid Pooling Networks

Micro-Expression-Based Emotion Recognition Using Waterfall Atrous Spatial Pyramid Pooling Networks

The Evolution of Language Models Applied to Emotion Analysis of Arabic Tweets

Identifying SME customers from click feedback on mobile banking apps: Supervised and semi-supervised approaches

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