The Multifaceted Concept of Context in Sentiment Analysis

Kumar, Akshi; Garg, Geetanjali

doi:10.1007/978-981-15-1451-7_44

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…However, detecting sarcasm on a textual level that is in written text is comparatively more demanding due to absence of such patent aural-visual cues, making it prone to misinterpretations. As a result, more attention needs to be paid to the semantic relationships between the words in the sentence which could elucidate the presence of certain incongruence and serve as potential indicators of sarcasm [4,10]. So reconsidering our example, "relax" and "weekend" are more likely a pair that will be used together with a positive verb like "love" or "like" than the pair of "work" and "weekend".…”

Section: Introductionmentioning

confidence: 96%

“…As a rhetorical literary device, sarcasm is highly subjective and contextual. It is vital to comprehend supplementary cues from users' linguistic input that are aware of 'context' to aid right interpretation [10]. So a sentence like "I love working on weekends" seems to have a positive connotation at first as someone is simply expressing his love towards a certain activity.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Deep Learning Model for Sarcasm Detection in Indian Indigenous Language Using Word-Emoji Embeddings

Kumar

Sangwan

Singh

et al. 2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Automated sarcasm detection is deemed as a complex natural language processing task and extending it to a morphologically-rich and free-order dominant indigenous Indian language Hindi is another challenge in itself. The scarcity of resources and tools such as annotated corpora, lexicons, dependency parser, Part-of-Speech tagger and benchmark datasets engorge the linguistic challenges of sarcasm detection in low-resource languages like Hindi. Furthermore, as context incongruity is imperative to detect sarcasm, various linguistic, aural and visual cues can be used to predict target utterance as sarcastic. While pre-trained word embeddings capture the meanings, semantic relationships and different types of contexts in the form of word representations, emojis can also render useful contextual information, analogous to human facial expressions, for gauging sarcasm. Thus, the goal of this research is to demonstrate the use of a hybrid deep learning model trained using two embeddings, namely word and emoji embeddings to detect sarcasm. The model is validated on a Hindi tweets dataset, Sarc-H, manually annotated with sarcastic and non-sarcastic labels. The preliminary results clearly depict the importance of using emojis for sarcasm detection, with our model attaining an accuracy of 97.35% with an F-score of 0.9708. The research validates that automated feature engineering facilitates efficient and repeatable predictive model for detecting sarcasm in indigenous, low-resource languages.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Model for Sarcasm Detection in Indian Indigenous Language Using Word-Emoji Embeddings

Kumar

Sangwan

Singh

et al. 2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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“…Different researchers have considered varied operational cues to typify context. In 2019, Kumar and Garg [7] defined five broad categories of context, namely, social-graph, temporal, content, modality, and user-profile based which can be used for improving the classification accuracy. Evidently, it is essential to capture the operational concern, that is, the pragmatic meaning defined by "context" as sarcasm.…”

Section: Introductionmentioning

confidence: 99%

Explainable Artificial Intelligence for Sarcasm Detection in Dialogues

Kumar

Dikshit

Albuquerque

2021

Wireless Communications and Mobile Computing

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Sarcasm detection in dialogues has been gaining popularity among natural language processing (NLP) researchers with the increased use of conversational threads on social media. Capturing the knowledge of the domain of discourse, context propagation during the course of dialogue, and situational context and tone of the speaker are some important features to train the machine learning models for detecting sarcasm in real time. As situational comedies vibrantly represent human mannerism and behaviour in everyday real-life situations, this research demonstrates the use of an ensemble supervised learning algorithm to detect sarcasm in the benchmark dialogue dataset, MUStARD. The punch-line utterance and its associated context are taken as features to train the eXtreme Gradient Boosting (XGBoost) method. The primary goal is to predict sarcasm in each utterance of the speaker using the chronological nature of a scene. Further, it is vital to prevent model bias and help decision makers understand how to use the models in the right way. Therefore, as a twin goal of this research, we make the learning model used for conversational sarcasm detection interpretable. This is done using two post hoc interpretability approaches, Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP), to generate explanations for the output of a trained classifier. The classification results clearly depict the importance of capturing the intersentence context to detect sarcasm in conversational threads. The interpretability methods show the words (features) that influence the decision of the model the most and help the user understand how the model is making the decision for detecting sarcasm in dialogues.

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“…Several Natural Language Processing (NLP) applications and tools are proliferating recently (Kumar and Garg, 2020), (Hirschberg and Manning, 2015), especially with the rise of Deep Learning (DL) and Machine Learning (ML) enhancements (Duerr and Ramdeen, 2017) (Young et al, 2018) . One of NLP state of the art approaches is ELMo language preprocessing model as a pretrained model on general NLP tasks of language modeling (Reimers and Gurevych, 2019).…”

Section: Introductionmentioning

confidence: 99%

ELMo-NB at SemEval-2020 Task 7: Assessing Sense of Humor in EditedNews Headlines Using ELMo and NB

Khwaileh

Al-Asa'd

2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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In this paper, we present our submission for SemEval-2020 competition subtask 1 in Task 7 (Hossain et al., 2020a): Assessing Humor in Edited News Headlines. The task consists of estimating the hilariousness of news headlines that have been modified manually by humans using micro-edit changes to make them funny. Our approach is constructed to improve on a couple of aspects; preprocessing with an emphasis on humor sense detection, using embeddings from state-of-the-art language model (ELMo), and ensembling the results came up with using machine learning model Naïve Bayes (NB) with a deep learning pretrained models. ELMo-NB participation has scored (0.5642) on the competition leader board, where results were measured by Root Mean Squared Error (RMSE).

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The Multifaceted Concept of Context in Sentiment Analysis

Cited by 4 publications

References 16 publications

Hybrid Deep Learning Model for Sarcasm Detection in Indian Indigenous Language Using Word-Emoji Embeddings

Hybrid Deep Learning Model for Sarcasm Detection in Indian Indigenous Language Using Word-Emoji Embeddings

Explainable Artificial Intelligence for Sarcasm Detection in Dialogues

ELMo-NB at SemEval-2020 Task 7: Assessing Sense of Humor in EditedNews Headlines Using ELMo and NB

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