Borrow from rich cousin: transfer learning for emotion detection using cross lingual embedding

Ahmad, Zishan; Jindal, Raghav; Ekbal, Asif; Bhattachharyya, Pushpak

doi:10.1016/j.eswa.2019.112851

Cited by 85 publications

(28 citation statements)

References 13 publications

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“…Ahmad et al 90 detected emotions using cross lingual embeddings in transfer learning. They built an emotion labeled dataset (Emo-Dis-HI) by crawling the Hindi news website to obtain disaster documents.…”

Section: Current State-of-the-art Text-based Proposalsmentioning

confidence: 99%

“…Finally, the cultural affiliations of an individual greatly influence their expressed emotions toward situations. However, there exist few emotion labeled resources for languages other than the English Language 90 . The availability of rich resources in other languages such as French, Spanish, Hindi, and so on can greatly change the narrative and encourage research in the field in order to balance work done in different languages.…”

Section: Open Issues and Future Research Directionsmentioning

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: Current State-of-the-art Text-based Proposalsmentioning

confidence: 99%

Section: Open Issues and Future Research Directionsmentioning

confidence: 99%

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

Acheampong

Chen

Nunoo‐Mensah

2020

Engineering Reports

236

View full text Add to dashboard Cite

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“…The enormous task for companies is to transform unstructured data into meaningful insights that can help them in decision-making (Ahmad et al. 2020 )…”

Section: Introductionmentioning

confidence: 99%

“…Ahmad et al. ( 2020 ) adopted the wheel of emotion modeled by Plutchik for labeling Hindi sentences with nine different Plutchik model states, decreasing semantic confusion, among other words. Plutchik and Ekman’s model's states are also utilized in various handcrafted lexicons like WordNet-Affect (Strapparava et al.…”

Section: Introductionmentioning

confidence: 99%

A review on sentiment analysis and emotion detection from text

Nandwani

Verma

2021

Soc. Netw. Anal. Min.

424

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Social networking platforms have become an essential means for communicating feelings to the entire world due to rapid expansion in the Internet era. Several people use textual content, pictures, audio, and video to express their feelings or viewpoints. Text communication via Web-based networking media, on the other hand, is somewhat overwhelming. Every second, a massive amount of unstructured data is generated on the Internet due to social media platforms. The data must be processed as rapidly as generated to comprehend human psychology, and it can be accomplished using sentiment analysis, which recognizes polarity in texts. It assesses whether the author has a negative, positive, or neutral attitude toward an item, administration, individual, or location. In some applications, sentiment analysis is insufficient and hence requires emotion detection, which determines an individual’s emotional/mental state precisely. This review paper provides understanding into levels of sentiment analysis, various emotion models, and the process of sentiment analysis and emotion detection from text. Finally, this paper discusses the challenges faced during sentiment and emotion analysis.

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“…Due to common communication features and evolving emoticon lexicons regardless of any language, emoticons become the universal language of communication [6], [7]. The deep learning models gain popularity with rise in the resources running these heavy models requiring large amount of training data [8], [9], [10], [11]. But these models are computationally costly and require resources affecting the environment [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Harnessing Emotive Features for Emotion Recognition from Text

Mahajan¹,

Zaveri²

2021

IJACSA

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With the prevalence of affective computing, emotion recognition becomes vital in any work related to natural language understanding. The inspiration for this work is provided by supplying machines with complete emotional intelligence and integrating them into routine life to satisfy complex human desires and needs. The text being a common communication medium on social media even now, it is important to analyze the emotions expressed in the text which is challenging due to the absence of audio-visual cues. Additionally, the conversational text conveys many emotions through communication contexts. Emoticon serves the purpose of selfannotation of writer's emotion in text. Therefore, a machine learning-based text emotion recognition model using emotive features proposed and evaluated it on the SemEval-2019 dataset. The proposed work involves exploitation of different emotionbased features with classical machine learning classifiers like SVM, Multilayer perceptron, REPTree, and decision tree classifiers. The proposed system performs competitively well in terms of f-score 65.31% and accuracy 87.55%.

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Borrow from rich cousin: transfer learning for emotion detection using cross lingual embedding

Cited by 85 publications

References 13 publications

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

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

A review on sentiment analysis and emotion detection from text

Harnessing Emotive Features for Emotion Recognition from Text

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