Emotion Detection from Text Using Skip-thought Vectors

Hassan, Maruf; Alam, Md. Sakib Bin; Ahsan, Tanveer

doi:10.1109/iciset.2018.8745615

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Machine learning [10][11][12][13][14][15] techniques can be broadly categorized into two groups, such as supervised learning and unsupervised [16] learning. Supervised learning is the most popular approach, where the class label of each training tuple is known.…”

Section: Methodsmentioning

confidence: 99%

Bank Deposit Prediction Using Ensemble Learning

Patwary

Akter

Alam

et al. 2021

Artificial Intelligence Evolution

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Bank deposit is one of the vital issues for any financial institution. It is very challenging to predict a customer if he/she can be a depositor by analyzing related information. Some recent reports demonstrate that economic depression and the continuous decline of the economy negatively impact business organizations and banking sectors. Due to such economic depression, banks cannot attract a customer's attention. Thus, marketing is preferred to be a handy tool for the banking sector to draw customers' attention for a term deposit. The purpose of this paper is to study the performance of ensemble learning algorithms which is a novel approach to predict whether a new customer will have a term deposit or not. A Portuguese retail bank data is used for our study, containing 45,211 phone contacts with 16 input attributes and one decision attribute. The data are preprocessed by using the Discretization technique. 40,690 samples are used for training the classifiers, and 4,521 samples are used for testing. In this work, the performance of the three mostly used classification algorithms named Support Vector Machine (SVM), Neural Network (NN), and Naive Bayes (NB) are analyzed. Then the ability of ensemble methods to improve the efficiency of basic classification algorithms is investigated and experimentally demonstrated. Experimental results exhibit that the performance metrics of Neural Network (Bagging) is higher than other ensemble methods. Its accuracy, sensitivity, and specificity are 96.62%, 97.14%, and 99.08%, respectively. Although all input attributes are considered in the classification method, in the end, a descriptive analysis has shown that some input attributes have more importance for this classification. Overall, it is shown that ensemble methods outperformed the traditional algorithms in this domain. We believe our contribution can be used as a depositor prediction system to provide additional support for bank deposit prediction.

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Section: Methodsmentioning

confidence: 99%

Bank Deposit Prediction Using Ensemble Learning

Patwary

Akter

Alam

et al. 2021

Artificial Intelligence Evolution

View full text Add to dashboard Cite

show abstract

“…However, the sentiment analysis and emotion detection were conducted in separated dataset. Another study about emotion was conducted by Hassan et al [6]. This study was emotion classification using Skip-thought Vector.…”

Section: Related Workmentioning

confidence: 99%

Aspect-Based Sentiment Analysis and Emotion Detection for Code-Mixed Review

Suciati

Budi²

2020

IJACSA

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Review can affect customer decision making because by reading it, people manage to know whether the review is positive, or negative. However, positive, negative, and neutral, without considering the emotion will be not enough because emotion can strengthen the sentiment result. This study explains about the comparison of machine learning and deep learning in sentiment as well as emotion classification with multilabel classification. In machine learning comparison, the problem transformation that we used are Binary Relevance (BR), Classifier Chain (CC), and Label Powerset (LP), with Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extra Tree Classifier (ET) as algorithms of machine learning. The features we compared are n-gram language model (unigram, bigram, unigram-bigram). For deep learning, algorithms that we applied are Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM), using selfdeveloped word embedding. The comparison results show RF dominates with 88.4% and 89.54% F1 scores with CC method for food aspect, and LP for price, respectively. For service and ambience aspects, ET leads with 92.65% and 87.1% with LP and CC methods, respectively. On the other hand, in deep learning comparison, GRU and BiLSTM obtained similar F1-score for food aspect, 88.16%. On price aspect, GRU leads with 83.01%. However, for service and ambience, BiLSTM achieved higher F1score, 89.03% and 84.78%.

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Deep learning modelling techniques: current progress, applications, advantages, and challenges

et al. 2023

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Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can be applied across various sectors. Specifically, it possesses the ability to utilize two or more levels of non-linear feature transformation of the given data via representation learning in order to overcome limitations posed by large datasets. As a multidisciplinary field that is still in its nascent phase, articles that survey DL architectures encompassing the full scope of the field are rather limited. Thus, this paper comprehensively reviews the state-of-art DL modelling techniques and provides insights into their advantages and challenges. It was found that many of the models exhibit a highly domain-specific efficiency and could be trained by two or more methods. However, training DL models can be very time-consuming, expensive, and requires huge samples for better accuracy. Since DL is also susceptible to deception and misclassification and tends to get stuck on local minima, improved optimization of parameters is required to create more robust models. Regardless, DL has already been leading to groundbreaking results in the healthcare, education, security, commercial, industrial, as well as government sectors. Some models, like the convolutional neural network (CNN), generative adversarial networks (GAN), recurrent neural network (RNN), recursive neural networks, and autoencoders, are frequently used, while the potential of other models remains widely unexplored. Pertinently, hybrid conventional DL architectures have the capacity to overcome the challenges experienced by conventional models. Considering that capsule architectures may dominate future DL models, this work aimed to compile information for stakeholders involved in the development and use of DL models in the contemporary world.

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Emotion Detection from Text Using Skip-thought Vectors

Cited by 5 publications

References 12 publications

Bank Deposit Prediction Using Ensemble Learning

Bank Deposit Prediction Using Ensemble Learning

Aspect-Based Sentiment Analysis and Emotion Detection for Code-Mixed Review

Deep learning modelling techniques: current progress, applications, advantages, and challenges

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