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

Goel, Pranav; Kulshreshtha, Devang; Jain, Prayas; Shukla, K. K.

doi:10.18653/v1/w17-5207

Cited by 45 publications

(33 citation statements)

References 21 publications

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“…In addition, the conclusion from their numerical experiments cannot be generalized to affective computing, since the authors labeled their dataset through a heuristic procedure and then reconstructed this heuristic with their classifier. Closest to our approach are experiments that include an LSTM for intensity estimation of emotions (Goel et al, 2017;Lakomkin et al, 2017;Meisheri et al, 2017;Zhang et al, 2017), but the results are limited to regression tasks where the presence of specific affective dimensions is given a priori.…”

Section: Deep Learningmentioning

confidence: 99%

Deep learning for affective computing: Text-based emotion recognition in decision support

Kratzwald

Ilić

Kraus

et al. 2018

Decision Support Systems

280

117

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Emotions widely affect human decision-making. This fact is taken into account by affective computing with the goal of tailoring decision support to the emotional states of individuals. However, the accurate recognition of emotions within narrative documents presents a challenging undertaking due to the complexity and ambiguity of language. Performance improvements can be achieved through deep learning; yet, as demonstrated in this paper, the specific nature of this task requires the customization of recurrent neural networks with regard to bidirectional processing, dropout layers as a means of regularization, and weighted loss functions. In addition, we propose sent2affect, a tailored form of transfer learning for affective computing: here the network is pre-trained for a different task (i.e. sentiment analysis), while the output layer is subsequently tuned to the task of emotion recognition. The resulting performance is evaluated in a holistic setting across 6 benchmark datasets, where we find that both recurrent neural networks and transfer learning consistently outperform traditional machine learning. Altogether, the findings have considerable implications for the use of affective computing.

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Section: Deep Learningmentioning

confidence: 99%

Deep learning for affective computing: Text-based emotion recognition in decision support

Kratzwald

Ilić

Kraus

et al. 2018

Decision Support Systems

280

117

View full text Add to dashboard Cite

show abstract

“…In terms of algorithms, various machine-learning models, such as Naïve Bayes [15,16], Ensemble [17], or Deep Learning Structure [18] have been used for crime prediction, but Deep Neural Networks (DNN) provided better results in our previous experiments. This study uses DNN because it reflects representation learning and has been used in crosslingual transfer [19], speech recognition [20][21][22][23], image recognition [24][25][26][27], sentiment analysis [28][29][30][31][32], and biomedical [33]. Although the upper bound of the prediction performance still depends on the problem and the data themselves, DNN's auto-feature extraction [34] allows us to use rapid model building without feature processing, thus reducing the application threshold due to feature processing.…”

Section: Related Workmentioning

confidence: 99%

Grid-Based Crime Prediction Using Geographical Features

Lin

Yen

2018

IJGI

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Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design's baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline.

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“…Three types of models were used in our system, a feed-forward neural network, an LSTM network and an SVM regressor. The neural nets were inspired by the work of Prayas (Goel et al, 2017) in the previous shared task. Different regression algorithms (e.g.…”

Section: Algorithms Usedmentioning

confidence: 99%

UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish

Kuijper¹,

Lenthe²,

Noord³

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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The present study describes our submission to SemEval 2018 Task 1: Affect in Tweets. Our Spanish-only approach aimed to demonstrate that it is beneficial to automatically generate additional training data by (i) translating training data from other languages and (ii) applying a semi-supervised learning method. We find strong support for both approaches, with those models outperforming our regular models in all subtasks. However, creating a stepwise ensemble of different models as opposed to simply averaging did not result in an increase in performance. We placed second (EI-Reg), second (EI-Oc), fourth (V-Reg) and fifth (V-Oc) in the four Spanish subtasks we participated in.

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

Cited by 45 publications

References 21 publications

Deep learning for affective computing: Text-based emotion recognition in decision support

Deep learning for affective computing: Text-based emotion recognition in decision support

Grid-Based Crime Prediction Using Geographical Features

UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish

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