Reviewing Classification Approaches in Sentiment Analysis

Yusof, Nor Nadiah; Mohamed, Azlinah; Abdul-Rahman, Shuzlina

doi:10.1007/978-981-287-936-3_5

Cited by 27 publications

(12 citation statements)

References 41 publications

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“…Generally, machine learning methods of SA (as opposed to lexicon-based approaches) attempt to evaluate text polarity based on train and test datasets (Birjali et al 2021). Such methods can be subdivided into supervised, semi-supervised, unsupervised, and reinforcement learning (Yusof et al 2015). Supervised learning is preferred for tasks with a specific set of classes, while unsupervised learning is used in the opposite case, i.e., when the author does not have any labeled data.…”

Section: Sentiment Analysis With Bertmentioning

confidence: 99%

Sentiment Analysis and Stance Detection on German YouTube Comments on Gender Diversity

Melnyk

Feld

2022

J. Comp. Assist. Linguist. Res.

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This paper explores different options of detecting the stance of German YouTube comments regarding the topic of gender diversity and compares the respective results with those of sentiment analysis, showing that these are two very different NLP tasks focusing on distinct characteristics of the discourse. While an already existing model was used to analyze the comments’ sentiment (BERT), the comments’ stance was first annotated and then used to train different models – SVM with TF-IDF, DistilBERT, LSTM and CNN – for predicting the stance of unseen comments. The best results were achieved by the CNN, reaching 78.3% accuracy (92% after dataset normalization) on the test set. Whereas the most common stance identified in the comments is a neutral one (neither completely in favor nor completely against gender diversity), the overall sentiment of the discourse turns out to be negative. This shows that the discourse revolving around the topic of gender diversity in YouTube comments is filled with strong opinions, on the one hand, but also opens up a space for anonymously inquiring and learning about the topic and its implications, on the other. Our research thereby (1) contributes to the understanding and application of different NLP tasks used to predict the sentiment and stance of unstructured textual data, and (2) provides relevant insights into society’s attitudes towards a changing system of values and beliefs.

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Section: Sentiment Analysis With Bertmentioning

confidence: 99%

Sentiment Analysis and Stance Detection on German YouTube Comments on Gender Diversity

Melnyk

Feld

2022

J. Comp. Assist. Linguist. Res.

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“…Machine learning techniques are particularly effective for classifying sentiments in positive, negative, or neutral types for classified document [10]. Training and testing datasets are needed in machine learning techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Sentiment Classification on Fake News Detection

Mahmud¹,

Shaeeali²,

Mutalib³

2021

IJACSA

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“…Lexicon-based and machine learning-based methods are two typical approaches for sentiment classification (Liu and Chen, 2015;Yusof et al, 2015). To improve the efficiency in industrial scenarios, the sentiment knowledge and lexicon-based word study are embedded into the sentimental classifier.…”

Section: Sentiment Classificationmentioning

confidence: 99%

What attracts vehicle consumers’ buying

Zhou

Lim

et al. 2019

IMDS

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Purpose The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

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Reviewing Classification Approaches in Sentiment Analysis

Cited by 27 publications

References 41 publications

Sentiment Analysis and Stance Detection on German YouTube Comments on Gender Diversity

Sentiment Analysis and Stance Detection on German YouTube Comments on Gender Diversity

Comparison of Machine Learning Algorithms for Sentiment Classification on Fake News Detection

What attracts vehicle consumers’ buying

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