Classifying Text-Based Emotions Using Logistic Regression

Alotaibi, Fahad Mazaed

doi:10.21015/vtcs.v16i2.551

Cited by 29 publications

(9 citation statements)

References 14 publications

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“…Alotaibi 52 classified emotions in ISEAR database. He preprocessed and trained the data on four classifiers and realized that logistic regression outperformed the other methods, that is, SVM, KNN, and the XG-Boost with a Precision of 86%, recall of 84%, and an F-Score of 85%.…”

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

confidence: 99%

“…Recently, supervised deep learning models are being adopted as ML approaches to detect emotions from texts. The argument has been because deep learning techniques are more robust and that their deep layers can extract the intrinsic/hidden details texts may carry 52 . The implementation of these techniques to texts‐based ED problems has been seen to outperform techniques that implemented TUML techniques 52,53 .…”

Section: Detection Approaches and Related Workmentioning

confidence: 99%

“…The argument has been because deep learning techniques are more robust and that their deep layers can extract the intrinsic/hidden details texts may carry 52 . The implementation of these techniques to texts‐based ED problems has been seen to outperform techniques that implemented TUML techniques 52,53 . Some works implementing some of these techniques breaking records in text‐based ED are discussed in the later sections.…”

Section: Detection Approaches and Related Workmentioning

confidence: 99%

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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%

Section: Detection Approaches and Related Workmentioning

confidence: 99%

Section: Detection Approaches and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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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“…We determine the likelihood that the output variable's perception corresponds to the proper category using LR [68]. The LR model is used to categorize emotions from inside the input text [69]. With training and testing sets, LR classifies text into several emotion categories [70].…”

Section: Algorithm 1 Racism Detection Tresholdsmentioning

confidence: 99%

Emotion Detection from Text and Sentiment Analysis of Ukraine Russia War using Machine Learning Technique

Maruf¹,

Ziyad²,

Haque³

et al. 2022

IJACSA

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In the human body, emotion plays a critical function. Emotion is the most significant subject in human-machine interaction. In economic contexts, emotion detection is equally essential. Emotion detection is crucial in making any decision. Several approaches were explored to determine emotion in text. People increasingly use social media to share their views, and researchers strive to decipher emotions from this medium. There has been some work on emotion detection from the text and sentiment analysis. Although some work has been done in which emotion has been recognized, there are many things to improve. There is not much work to detect racism and analysis sentiment on Ukraine -Russia war. We suggested a unique technique in which emotion is identified, and the sentiment is analyzed. We utilized Twitter data to analyze the sentiment of the Ukraine-Russia war. Our system performs better than prior work. The study increases the accuracy of detecting emotion. To identify emotion and racism, we used classical machine learning and the ensemble method. An unsupervised approach and NLP modules were used to analyze sentiment. The goal of the study is to detect emotion and racism and also analyze the sentiment.

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“…In comparison to WordNet in the ISEAR database, the VSM approach performs better. Another Logistic Regression (LR)-based model was considered by Alotaibi [22]; according to the proposed model, the preprocessing data has been trained in four classifications. It has been found out that logistic regression outperformed the other methods.…”

Section: Introductionmentioning

confidence: 99%

Speech Emotion Recognition Using ANFIS and PSO-optimization With Word2Vec

Rezaie

Parnianifard

Rodríguez

et al. 2022

Preprint

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Speech Emotion Recognition (SER) plays a vital role in human-computer interaction as an important branch of affective computing. Due to inconsistencies in the data and challenging signal extraction, in this paper, we propose a novel emotion recognition method based on the combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) with Word to Vector (Word2Vec) models. To begin, the inputs have been pre-processed, which comprise audio and text data. Second, the features were extracted using the Word2vec behind spectral and prosodic approaches. Finally, the features are selected using the Sequential Backward Floating Selection (SBFS) approach. In the end, the ANFIS-PSO model has been used to recognize speech emotion. A performance evaluation of the proposed algorithm is carried out on Sharif Emotional Speech Database (ShEMO). The experimental results show that the proposed algorithm has advantages in accuracy, reaching 0.873 and 0.752 in males and females, respectively, in comparison with the CNNs and SVM, MLP, RF models.

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Classifying Text-Based Emotions Using Logistic Regression

Cited by 29 publications

References 14 publications

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

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

Emotion Detection from Text and Sentiment Analysis of Ukraine Russia War using Machine Learning Technique

Speech Emotion Recognition Using ANFIS and PSO-optimization With Word2Vec

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