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

Acheampong, Francisca Adoma; Chen, Wenyu; Nunoo‐Mensah, Henry

doi:10.1002/eng2.12189

Cited by 220 publications

(148 citation statements)

References 82 publications

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“…We found only a small number of articles focused on emotion detection. We feel that there is a greater need to take into consideration the emotions expressed in opinions to better identify and address the issues related towards the target subject, as has been investigated in many other text-based emotion detection works [103]. Furthermore, there are standard publicly available datasets such as ISEAR (https://www.kaggle.com/shrivastava/isearsdataset), and SemEval-2019 [104] that can be used to train deep learning models for textbased emotion detection tasks utilizing the Plutchik model [1] coupled with emoticons [8].…”

Section: Emotion Detectionmentioning

confidence: 99%

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

et al. 2021

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In the last decade, sentiment analysis has been widely applied in many domains, including business, social networks and education. Particularly in the education domain, where dealing with and processing students’ opinions is a complicated task due to the nature of the language used by students and the large volume of information, the application of sentiment analysis is growing yet remains challenging. Several literature reviews reveal the state of the application of sentiment analysis in this domain from different perspectives and contexts. However, the body of literature is lacking a review that systematically classifies the research and results of the application of natural language processing (NLP), deep learning (DL), and machine learning (ML) solutions for sentiment analysis in the education domain. In this article, we present the results of a systematic mapping study to structure the published information available. We used a stepwise PRISMA framework to guide the search process and searched for studies conducted between 2015 and 2020 in the electronic research databases of the scientific literature. We identified 92 relevant studies out of 612 that were initially found on the sentiment analysis of students’ feedback in learning platform environments. The mapping results showed that, despite the identified challenges, the field is rapidly growing, especially regarding the application of DL, which is the most recent trend. We identified various aspects that need to be considered in order to contribute to the maturity of research and development in the field. Among these aspects, we highlighted the need of having structured datasets, standardized solutions and increased focus on emotional expression and detection.

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Section: Emotion Detectionmentioning

confidence: 99%

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

et al. 2021

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“…13 https://taku910.github.io/mecab/ Following the standard emotional intensity estimation models (Acheampong et al, 2020), we train the following three types of four-class classification models for each emotion.…”

Section: Experimental Settingsmentioning

confidence: 99%

WRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations

Kajiwara¹,

Chu²,

Takemura³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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We annotate 17,000 SNS posts with both the writer's subjective emotional intensity and the reader's objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer's subjective labels than the readers'. The large gap between the subjective and objective emotions implies the complexity of the mapping from a post to the subjective emotional intensities, which also leads to a lower performance with machine learning models.2 EmoBank (Buechel and Hahn, 2017) is a dataset that aims to collect the emotional intensity of both writers and readers. However, crowdsourcing annotators, who are different from the text writer, infer the writer's emotions, so they are not able to collect the writer's subjective emotions.3 Dataset of writers' and readers' intensities of emotion for their estimation. An expanded version of 40,000 posts is available.

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“…Most of these data-driven approaches require a high fidelity and quality training dataset. There are several publicly available datasets as reported by [7]. These datasets are valuable and cover broad spectrums such as cross-cultural studies, news, tales, narratives, dialogues, and utterances.…”

Section: Natural Language Processing For Sentiment Analysismentioning

confidence: 99%

“…Emotion detection is a subset of sentiment analysis that seeks not only polarity (e.g., positive, negative, or neutral) from input sentence or speech but tries to derive more detailed emotions (e.g., happy, sad, anxious, nervous instead positive or negative). Regarding this topic, there is an interesting survey report [7]. This comprehensive survey focuses on conventional rule-based and current deep learning-based approaches.…”

Section: Emotion Detection In Sentiment Analysismentioning

confidence: 99%

“…Sentiment can be efficiently detected by exploiting multimodal sensory information such as voice, gestures, images, or heartbeats, as evident in well-established literature and studies in physiology and neurosciences [7]. However, reasoning sentiment from text information such as tweets, blog posts, and product reviews is a more difficult problem.…”

Section: Introductionmentioning

confidence: 99%

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Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models

Lim

Ahn

et al. 2021

Sensors

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Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience, and computer science. This stems from its high degree of subjectivity and limited input sources that can effectively capture the actual sentiment. This can be even more challenging with only text-based input. Meanwhile, the rise of deep learning and an unprecedented large volume of data have paved the way for artificial intelligence to perform impressively accurate predictions or even human-level reasoning. Drawing inspiration from this, we propose a coverage-based sentiment and subsentence extraction system that estimates a span of input text and recursively feeds this information back to the networks. The predicted subsentence consists of auxiliary information expressing a sentiment. This is an important building block for enabling vivid and epic sentiment delivery (within the scope of this paper) and for other natural language processing tasks such as text summarisation and Q&A. Our approach outperforms the state-of-the-art approaches by a large margin in subsentence prediction (i.e., Average Jaccard scores from 0.72 to 0.89). For the evaluation, we designed rigorous experiments consisting of 24 ablation studies. Finally, our learned lessons are returned to the community by sharing software packages and a public dataset that can reproduce the results presented in this paper.

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

Cited by 220 publications

References 82 publications

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

WRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations

Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models

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