Intent Classification for Dialogue Utterances

Schuurmans, Jetze; Frăsincar, Flavius; Cambria, Erik

doi:10.1109/mis.2019.2954966

Cited by 52 publications

(17 citation statements)

References 19 publications

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“…This approach improves the prediction accuracies in speaker independent sentiment intensity analysis. Multiple other recent studies under the topic of unimodal and multimodal sentiment or emotion analysis are reported in [30][31][32][33][34][35][36][37][38][39][40][41].…”

Section: Background and Literature Review On Multimodal Emotion Rmentioning

confidence: 99%

Cross-Subject Multimodal Emotion Recognition Based on Hybrid Fusion

2020

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Multimodal emotion recognition has gained traction in affective computing research community to overcome the limitations posed by the processing a single form of data and to increase recognition robustness. In this study, a novel emotion recognition system is introduced, which is based on multiple modalities including facial expressions, galvanic skin response (GSR) and electroencephalogram (EEG). This method follows a hybrid fusion strategy and yields a maximum one-subject-out accuracy of 81.2% and a mean accuracy of 74.2% on our bespoke multimodal emotion dataset (LUMED-2) for 3 emotion classes: sad, neutral and happy. Similarly, our approach yields a maximum one-subject-out accuracy of 91.5% and a mean accuracy of 53.8% on the Database for Emotion Analysis using Physiological Signals (DEAP) for varying numbers of emotion classes, 4 in average, including angry, disgust, afraid, happy, neutral, sad and surprised. The presented model is particularly useful in determining the correct emotional state in the case of natural deceptive facial expressions. In terms of emotion recognition accuracy, this study is superior to, or on par with, the reference subject-independent multimodal emotion recognition studies introduced in the literature.

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Section: Background and Literature Review On Multimodal Emotion Rmentioning

confidence: 99%

Cross-Subject Multimodal Emotion Recognition Based on Hybrid Fusion

2020

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“…For the problem of intent classification for dialogue utterances, ref. [44] investigated several machine learning methods, concluding that SVM obtained the best results based on the macro-F1 metric. Ref.…”

Section: Recent Sentiment Analysis Approachesmentioning

confidence: 99%

Bayesian Constitutionalization: Twitter Sentiment Analysis of the Chilean Constitutional Process through Bayesian Network Classifiers

2022

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Constitutional processes are a cornerstone of modern democracies. Whether revolutionary or institutionally organized, they establish the core values of social order and determine the institutional architecture that governs social life. Constitutional processes are themselves evolutionary practices of mutual learning in which actors, regardless of their initial political positions, continuously interact with each other, demonstrating differences and making alliances regarding different topics. In this article, we develop Tree Augmented Naive Bayes (TAN) classifiers to model the behavior of constituent agents. According to the nature of the constituent dynamics, weights are learned by the model from the data using an evolution strategy to obtain a good classification performance. For our analysis, we used the constituent agents’ communications on Twitter during the installation period of the Constitutional Convention (July–October 2021). In order to differentiate political positions (left, center, right), we applied the developed algorithm to obtain the scores of 882 ballots cast in the first stage of the convention (4 July to 29 September 2021). Then, we used k-means to identify three clusters containing right-wing, center, and left-wing positions. Experimental results obtained using the three constructed datasets showed that using alternative weight values in the TAN construction procedure, inferred by an evolution strategy, yielded improvements in the classification accuracy measured in the test sets compared to the results of the TAN constructed with conditional mutual information, as well as other Bayesian network classifier construction approaches. Additionally, our results may help us to better understand political behavior in constitutional processes and to improve the accuracy of TAN classifiers applied to social, real-world data.

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“…For instance, Henfy et al [5] conducted a study for developing an intent classifier of chat messages that were used in communicating between the teams of software developers. Additionally, Schuurmans & F. Frasincar [6] used several Machine Language algorithms for improving the intent classification of dialogue utterances. Furthermore, Pérez-Vera et al [7] developed a chatbot classifier to answer to the users' FAQ by using 5000 tweets from the twitter's account of one of the electricity's companies as it can enhance their services, accordingly increasing the customers' satisfaction.…”

Section: Chatbot For Intent Classificationmentioning

confidence: 99%

A Chatbot Intent Classifier for Supporting High School Students

Assayed

Shaalan

Alkhatib

2022

EAI Endorsed Scal Inf Syst

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INTRODUCTION: An intent classification is a challenged task in Natural Language Processing (NLP) as we are asking the machine to understand our language by categorizing the users’ requests. As a result, the intent classification plays an essential role in having a chatbot conversation that understand students’ requests. OBJECTIVES: In this study, we developed a novel chatbot called “HSchatbot” for predicting the intent classifications from high school students’ enquiries. Evidently, students in high schools are the most concerned among all students about their future; thus, in this stage they need an instant support in order to prepare them to take the right decision for their career choice. METHODS: The authors in this study used the Multinomial Naive-Bayes and Random Forest classifiers for predicting the students’ enquiries, which in turn improved the performance of the classifiers by using the feature’s extractions. RESULTS: The results show that the random forest classifier performed better than Multinomial Naive-Bayes since the performance of this model is checked by using different metrics like accuracy, precision, recall and F1 score. Moreover, all showed high accuracy scores exceeding 90% in all metrics. However, the accuracy of Multinomial Naive-Bayes classifier performed much better when using CountVectorizers compared to using the TF-IDF. CONCLUSION: In the future work, the results will be analysed and investigated in order to figure out the main factors that affect the performance of Multinomial Naive-Bayes classifier, as well as evaluating the model with using a large corpus of students’ questions and enquiries.

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Intent Classification for Dialogue Utterances

Cited by 52 publications

References 19 publications

Cross-Subject Multimodal Emotion Recognition Based on Hybrid Fusion

Cross-Subject Multimodal Emotion Recognition Based on Hybrid Fusion

Bayesian Constitutionalization: Twitter Sentiment Analysis of the Chilean Constitutional Process through Bayesian Network Classifiers

A Chatbot Intent Classifier for Supporting High School Students

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