Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

Asghar, Muhammad Zubair; Ullah, Ikram; Shamshirband, Shahab; Kundi, Fazal Masud; Habib, Ammara

doi:10.20944/preprints201907.0006.v1

Cited by 11 publications

(10 citation statements)

References 15 publications

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“…Mixture of datasets [34,42,43,47,49,53,67,68,85,97,98] In this category, we grouped all those studies which used several datasets to conduct their experiments RQ6. What are the solutions with respect to the packages, tools, frameworks and libraries utilized for sentiment analysis?…”

Section: Rq5 What Are the Most Common Sources Used To Collect Students' Feedback?mentioning

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: Rq5 What Are the Most Common Sources Used To Collect Students' Feedback?mentioning

confidence: 99%

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

et al. 2021

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“…The presented approach handles the unstructured data effectively and analyzes the individual data without any privacy issues. Asghar Park et al, 33 presented a fuzzy based sentiment analysis which classifies the student feedback to extract the opinions. The presented approach computes a score from the feedback and analyze using fuzzy logic.…”

Section: Related Workmentioning

confidence: 99%

A novel hybrid deep learning model for aspect based sentiment analysis

Kuppusamy

Selvaraj²

2022

Concurrency and Computation

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Summary The usage of social media, forums, and e‐commerce websites have been widely increased. Feedback from customers has a big impact on the final product. A service provider, merchant, or manufacturer need all the information, even if it is just a comment or a review about a service or a product. So, it is vital to look at input from users, and therefore sentiment analysis has received a lot of interest. Sentiment analysis is a method for identifying and analyzing text in order to determine the features, qualities, and viewpoints of particular user. Extracting user aspects is the main part of this process, and it is used to group the user aspects. In recent years, convolutional neural network (CNN) models have gained popularity in natural language processing. Thus, this research proposes a novel hybrid CNN model by concatenating the bidirectional long short‐term memory and CNN models to process the data sequentially by learning their high‐level features. The concatenated method minimizes the loss of critical information. Benchmark product reviews and hotel review datasets are employed in the experiments, and accuracies of 93.6% for the product review dataset and 92.7% for the hotel review dataset are achieved by the proposed hybrid model when compared to state‐of‐the‐art techniques.

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“…In this study, the output of the convolutional neural network model as in [20] was modified to be a fuzzy sentiment value. In other studies, the concept of fuzzy logic is used to classify text sentiment classes and successfully outperform other classification methods [23]. But the results of the method are not presented in the form of degrees of sentiment.…”

Section: -8mentioning

confidence: 99%

“…In other studies, fuzzy logic is applied to the initial domain in the fuzzy convolutional neural networks method [17]. Sentiment analysis developed based on fuzzy logic has also been investigated [18][19][20][21][22][23]. This shows that fuzzy logic in sentiment analysis is very relevant to be developed at this time.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy sentiment analysis using convolutional neural network

Sugiyarto

Eliyanto

Irsalinda

et al. 2021

AIP Conference Proceedings

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Sentiment analysis is one part of natural language processing. Sentiment analysis can be done by lexicon based, or machine learning based. Sentiment analysis based on machine learning has advantage of dynamism to meet with new language datasets or new vocabulary. Sentiment analysis seeks to understand the sentiments contained in a sentence. A sentence can be positive, neutral or negative, based on its sentiments. A sentence can have positive, neutral or negative sentiments. However, the fact is each sentence does not always have positive, negative or neutral sentiment clearly. We try to develop a sentiment analysis method that can show the sentiment degree of a sentence. Fuzzy sentiment analysis using convolutional neural network are introduced in this paper to produce more accurate sentiment analysis results. Convolutional neural networks are a popular machine learning method for sentiment analysis. The concept of fuzzy sets is used to express the sentiment degree of a sentence. Euclidean distance analysis to determine the proximity of two vectors is used to show that this method is better than the standard method. The method we propose successfully produces a value that indicates the degree of sentiment of a sentence. Comparison of the euclid distance between the results of the standard sentiment analysis and our method shows that the results of the fuzzy sentiment analysis using convolutional neural network have a distance that is relatively close to the true sentiment value. Fuzzy convolutional neural network analysis sentiment is proven to be able to produce better and smoother sentiment analysis results than standard methods.

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Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

Cited by 11 publications

References 15 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

A novel hybrid deep learning model for aspect based sentiment analysis

Fuzzy sentiment analysis using convolutional neural network

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