Actionable Pattern Discovery for Tweet Emotions

Tzacheva, Angelina A.; Ranganathan, Jaishree; Mylavarapu, Sai Yesawy

doi:10.1007/978-3-030-20454-9_5

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…Tzacheva et al 97 used the NRC emotion lexicon to label twitter data obtained using Twitter API into emotion classes. Then classified emotions using the SVM.…”

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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“…Tzacheva et al 97 used the NRC emotion lexicon to label twitter data obtained using Twitter API into emotion classes. Then classified emotions using the SVM.…”

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

View full text Add to dashboard Cite

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“…In this work we automatically detect user emotion from tweet data using the NRC Emotion Lexicon [11], [12] to label the Emotion class for our data. We use Support Vector Machine LibLinear implementation and features including word n-grams, character n-grams, brown word clusters, and part-of-speech tags and achieve a 10% improved accuracy compared to the previous method [33]. Also we extract action rules to identify what factors can be improved in order for a user to attain more desirable positive emotion or feeling.…”

Section: Discussionmentioning

confidence: 99%

Emotion Mining in Social Media Data

Ranganathan

Tzacheva

2019

Computer Science &Amp; Information Technology (CS &Amp; IT )

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Emotions are known to influence the perception of human beings along with their memory, thinking and imagination. Human perception is important in today's world in a wide range of factors including but not limited to business, education, art, and music. Microblogging and Social networking sites like Twitter, Facebook are challenging sources of information that allow people to share their feelings and thoughts on a daily basis. In this paper we propose an approach to automatically detect emotions on Twitter messages that explores characteristics of the tweets and the writer's emotion using Support Vector Machine LibLinear model and achieve 98% accuracy. Emotion mining gained attraction in the field of computer science due to the vast variety of systems that can be developed and promising applications like remote health care system, customer care services, smart phones that react based on users's emotion, vehicles that sense emotion of the driver. These emotions help understand the current state of user. In order to perform suitable actions or provide suggestions on how user's can enhance their feeling for a better healthy lifestyle we use actionable recommendations. In this work we extract action rules with respect to the user emotions that help provide suggestions for user's.

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“…The NRC emotion lexicon to classify Twitter data collected from the Twitter API. 42 The SVM was then used to identify feelings. After that, they found activity patterns and used action laws to reclassify and reclassify negative or neutral emotion states as positive.…”

Section: Literature Surveymentioning

confidence: 99%

A novel mathematical modeling in shift in emotion for gauging the social influential in big data streams with hybrid sarcasm detection

Kumaran

Chitrakala

2021

Concurrency and Computation

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Online social network is a platform that plays an essential role in identifying the emotional values of user-generated content such as blogs, posts, and comments along with their influential factors. Especially on Twitter, network users are growing worldwide day by day and creating a massive amount of data that is not analyzed effectively in a quick way. Identifying the most influential persons on the social network is also a challenging task over the wide range of real-time applications like recommendation systems. Now, to handle these situations, this article proposes a novel approach for prediction of information diffusion that includes emotion recognition with sarcasm detection based influence spreader identification (PID-ERSDISI). The proposed method uses the user-generated posts for emotion recognition in tandem with sarcasm detection both implicitly and explicitly. This approach helps to gauge the leverage that influences spreaders and also enhances the prediction accuracy of information diffusion in a better way. The implementation of the proposed work executed their task one after another in the following way, namely, sarcasm detection, emotional-level computation, breakpoint computation, breakpoint validation, influential user generation, and information diffusion. After the successful implementation of this proposed PID-ERSDIS, it produced prominent results against other state-of-art methods.

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Actionable Pattern Discovery for Tweet Emotions

Cited by 11 publications

References 14 publications

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

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

Emotion Mining in Social Media Data

A novel mathematical modeling in shift in emotion for gauging the social influential in big data streams with hybrid sarcasm detection

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