Automatic Detection of Insulting Sentences in Conversation

Allouch, Merav; Azaria, Amos; Azoulay, Rina; Ben-Izchak, Ester; Zachor, Ditza A.

doi:10.1109/icsee.2018.8646165

Cited by 18 publications

(13 citation statements)

References 9 publications

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“…Notwithstanding the challenges, the field has contributed massively to an improved human-computer interaction. These include the detection and offering of untimely assistance to individuals who may be considered suicidal, 7,8 detecting insulting sentences in conversations, 10 chatbots for psychiatric counseling, 29 and so on, although these are still at their infantile stage.…”

Section: F I G U R Ementioning

confidence: 99%

“…The introduction of transformers-based embeddings has shown a significant increase in the quality of contextual information extraction. 10,87,89 However, the use of transformers is affected by some limitations, viz., out of vocabulary (OOV) limitation, increased complexity, and importantly the method overfitting in small networks. 99,100 Due to the highlighted drawbacks of transformers, an ensemble of attention and neuro-fuzzy networks 101 would help reduce the limiting effects, thus increasing classification performance.…”

Section: Open Issues and Future Research Directionsmentioning

confidence: 99%

“…Allouch et al 10 developed a system that aided children to communicate well by detecting insulting words in conversations using an ML approach. They manually built a dataset consisting of 1241 non‐insulting and 1255 insulting sentences.…”

Section: Detection Approaches and Related Workmentioning

confidence: 99%

“…A major issue identified is the necessity to propose a robust technique for extracting this contextual information from text. The introduction of transformers‐based embeddings has shown a significant increase in the quality of contextual information extraction 10,87,89 . However, the use of transformers is affected by some limitations, viz., out of vocabulary (OOV) limitation, increased complexity, and importantly the method overfitting in small networks 99,100 .…”

Section: Open Issues and Future Research Directionsmentioning

confidence: 99%

“…The field of ED has also be applied in applications such as emotion retrieval from suicide notes, 7,8 capturing emotions in multimedia tagging, 9 detecting insulting sentences in conversations, 10 and so on. However, whereas detecting emotions from voice/speech, images, and other multimodal methods have an exhaustive knowledge base, there exists great paucity in research for texts.…”

Section: Introductionmentioning

confidence: 99%

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Text‐based emotion detection: Advances, challenges, and opportunities

Acheampong

Chen

Nunoo‐Mensah

2020

Engineering Reports

236

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Emotion detection (ED) is a branch of sentiment analysis that deals with the extraction and analysis of emotions. The evolution of Web 2.0 has put text mining and analysis at the frontiers of organizational success. It helps service providers provide tailor-made services to their customers. Numerous studies are being carried out in the area of text mining and analysis due to the ease in sourcing for data and the vast benefits its deliverable offers. This article surveys the concept of ED from texts and highlights the main approaches adopted by researchers in the design of text-based ED systems. The article further discusses some recent state-of-the-art proposals in the field. The proposals are discussed in relation to their major contributions, approaches employed, datasets used, results obtained, strengths, and their weaknesses. Also, emotion-labeled data sources are presented to provide neophytes with eligible text datasets for ED. Finally, the article presents some open issues and future research direction for text-based ED. K E Y W O R D S emotion detection, natural language processing, sentiment analysis, text-based emotion detection This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: F I G U R Ementioning

confidence: 99%

Section: Open Issues and Future Research Directionsmentioning

confidence: 99%

Section: Detection Approaches and Related Workmentioning

confidence: 99%

Section: Open Issues and Future Research Directionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Text‐based emotion detection: Advances, challenges, and opportunities

Acheampong

Chen

Nunoo‐Mensah

2020

Engineering Reports

236

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Emotion Extraction from Text Using Fuzzy-Deep Neural Network

Zaremarjal

Yiltas-Kaplan

Lazemi

2021

Lecture Notes in Networks and Systems

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Emotions are one of the most important and affective start points of our decisions in daily life. People express their emotions, consciously or unconsciously, in their behaviors, like speech, facial expressions, textual content, and so on, whether it be in blogs, reviews, or on social media. Due to the rapid growth in textual social media to link people, the emotion extraction from textual data has attracted a lot of attention. This paper performs the sentence-level discrete emotion extraction from text by using Fuzzy Neural Networks (FNN) and Deep Recurrent Neural Networks (DRNN). Much work has been presented to extracting emotion from textual data with precise and constant boundaries, but little of it has considered the inherent uncertainty in natural language. In other words, the high ambiguity of emotion in text data makes an emotionally sentence express multiple emotions at the same time. To deal with this uncertainty we use a Neuro Neural Network (NNN). But from another point of view, by increasing the number of inputs, the number of parameters in NNN growth exponentially. To this end, we extract a low-dimensional semantic representation of input sentence by using a Bi-directional Long Short-Term Memory (Bi-LSTM). Our goal is to achieve better performance with combining the advantages of deep learning in high-level semantic feature extraction to ambiguity handling and the capabilities of fuzzy logic with fuzzy membership degrees to uncertainty handling. Experiments are conducted on the SemEval2007-Task14 dataset that contains 1250 annotated texts with Ekman's basic emotions. The obtained results indicate that our proposed method outperforms the previous methods.

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Deep learning for prediction of depressive symptoms in a large textual dataset

Uddin

Dysthe

Følstad

et al. 2021

Neural Comput & Applic

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Depression is a common illness worldwide with potentially severe implications. Early identification of depressive symptoms is a crucial first step towards assessment, intervention, and relapse prevention. With an increase in data sets with relevance for depression, and the advancement of machine learning, there is a potential to develop intelligent systems to detect symptoms of depression in written material. This work proposes an efficient approach using Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to identify texts describing self-perceived symptoms of depression. The approach is applied on a large dataset from a public online information channel for young people in Norway. The dataset consists of youth’s own text-based questions on this information channel. Features are then provided from a one-hot process on robust features extracted from the reflection of possible symptoms of depression pre-defined by medical and psychological experts. The features are better than conventional approaches, which are mostly based on the word frequencies (i.e., some topmost frequent words are chosen as features from the whole text dataset and applied to model the underlying events in any text message) rather than symptoms. Then, a deep learning approach is applied (i.e., RNN) to train the time-sequential features discriminating texts describing depression symptoms from posts with no such descriptions (non-depression posts). Finally, the trained RNN is used to automatically predict depression posts. The system is compared against conventional approaches where it achieved superior performance than others. The linear discriminant space clearly reveals the robustness of the features by generating better clustering than other traditional features. Besides, since the features are based on the possible symptoms of depression, the system may generate meaningful explanations of the decision from machine learning models using an explainable Artificial Intelligence (XAI) algorithm called Local Interpretable Model-Agnostic Explanations (LIME). The proposed depression symptom feature-based approach shows superior performance compared to the traditional general word frequency-based approaches where frequency of the features gets more importance than the specific symptoms of depression. Although the proposed approach is applied on a Norwegian dataset, a similar robust approach can be applied on other depression datasets developed in other languages with proper annotations and symptom-based feature extraction. Thus, the depression prediction approach can be adopted to contribute to develop better mental health care technologies such as intelligent chatbots.

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Automatic Detection of Insulting Sentences in Conversation

Cited by 18 publications

References 9 publications

Text‐based emotion detection: Advances, challenges, and opportunities

Text‐based emotion detection: Advances, challenges, and opportunities

Emotion Extraction from Text Using Fuzzy-Deep Neural Network

Deep learning for prediction of depressive symptoms in a large textual dataset

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