Sentence-Level Emotion Detection from Text Based on Semantic Rules

Seal, Dibyendu Bikash; Roy, Uttam Kumar; Basak, Rohini

doi:10.1007/978-981-13-7166-0_42

Cited by 50 publications

(13 citation statements)

References 3 publications

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“…Seal et al 65 's work was based on detecting emotions using semantic rules and emotion keywords with particular attention to phrasal verbs. They collected data from the ISEAR database, preprocessed the data, and analyzed them to detect phrasal verbs.…”

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

confidence: 99%

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

Acheampong

Chen

Nunoo‐Mensah

2020

Engineering Reports

235

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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%

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

Acheampong

Chen

Nunoo‐Mensah

2020

Engineering Reports

235

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“…Emotion is a complex state of mind, which represents the feeling of a person and which can influence both the physical and psychological behaviour of that individual [10]. Happiness, sadness, love, and hatred are some examples of emotions expressed by human beings.…”

Section: Methodsmentioning

confidence: 99%

Emotions in texts

Pokhun

Chuttur

2020

Bull.Socinf.The.App

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Several studies have used different techniques to detect and identify emotions expressed in various sets of texts corpora. In this paper, we review different emotion models, emotion datasets and the corresponding techniques used for emotion analysis in past studies. We observe that researchers have been using a wide variety of techniques to detect emotions in texts and that there is currently no gold standard on which dataset or which emotion model to use. Consequently, although the field of emotion analysis has gained much momentum in previous years, there seems to be little progress into relevant research with findings that may be useful in real world applications. From our analysis and findings, we urge researchers to consider the development of datasets, evaluation benchmarks and a common platform for sharing achievements in emotion analysis to see further development in the field.

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“…They have categorized sentences into 6 groups based on emotions and used TLBO technique to help the users in prioritizing their messages based on the emotions attached with the message. Seal et al (2020) [ 120 ] proposed an efficient emotion detection method by searching emotional words from a pre-defined emotional keyword database and analyzing the emotion words, phrasal verbs, and negation words. Their proposed approach exhibited better performance than recent approaches.…”

Section: Nlp: Then and Nowmentioning

confidence: 99%

Natural language processing: state of the art, current trends and challenges

Khurana

Koli

Khatter

et al. 2022

Multimed Tools Appl

718

134

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Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of N atural L anguage G eneration followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP.

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Sentence-Level Emotion Detection from Text Based on Semantic Rules

Cited by 50 publications

References 3 publications

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

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

Emotions in texts

Natural language processing: state of the art, current trends and challenges

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