Performance Analysis of Hyperparameters on a Sentiment Analysis Model

Kandhro, Irfan Ali; Jumani, Sahar Zafar; Ali, Fawad; Shaikh, Zarina; Arain, M. A.; Shaikh, Aftab Ahmed

doi:10.48084/etasr.3549

Cited by 12 publications

(6 citation statements)

References 20 publications

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“…Sentiment analysis processes and identifies the data of certain domains through natural language processing. Sentiment analysis using social media plays a crucial role in various fields such as social development, people's awareness, economic development [10][11][12]. Indeed, many studies applied ML, and Deep Learning (DL) methods and algorithms to explore and investigate the public's sentiments on social media platforms towards the outbreak of COVID-19 and its emerging variants [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Public Tweets Towards the Emergence of SARS-CoV-2 Omicron Variant: A Social Media Analytics Framework

Mahyoob

Algaraady

Alrahiali

et al. 2022

Eng. Technol. Appl. Sci. Res.

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While different variants of COVID-19 dramatically affected the lives of millions of people across the globe, a new version of COVID-19, "SARS-CoV-2 Omicron," emerged. This paper analyzes the public attitude and sentiment towards the emergence of the SARS-CoV-2 Omicron variant on Twitter. The proposed approach relies on the text analytics of Twitter data considering tweets, retweets, and hashtags' main themes, the pandemic restriction, the efficacy of covid-19 vaccines, transmissible variants, and the surge of infection. A total of 18,737 tweets were pulled via Twitter Application Programming Interface (API) from December 3, 2021, to December 26, 2021, using the SentiStrength software that employs a lexicon of sentiment terms and a set of linguistic rules. The analysis was conducted to distinguish and codify subjective content and estimate the strength of positive and negative sentiment with an average of 95% confidence intervals based upon emotion strength scales of 1-5. It is found that negativity was dominated after the outbreak of Omicron and scored 31.01% for weak, 16.32% for moderate, 5.36% for strong, and 0.35% for very strong sentiment strength. In contrast, positivity decreased gradually and scored 16.48% for weak, 11.19% for moderate, 0.80% for strong, and 0.04% for very strong sentiment strength. Identifying the public emotional status would help the concerned authorities to provide appropriate strategies and communications to relieve public worries about pandemics.

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Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Public Tweets Towards the Emergence of SARS-CoV-2 Omicron Variant: A Social Media Analytics Framework

Mahyoob

Algaraady

Alrahiali

et al. 2022

Eng. Technol. Appl. Sci. Res.

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“…Table 8 shows papers that reported the sources of the datasets used for conducting experiments along with their corresponding categories and description. Here, the data were mostly collected by conducting surveys among students and teachers or by providing questioners to collect feedback from the students Education/research platforms [14,31,36,40,[44][45][46]48,58,61,70,78,82,84,86,93,95,99,101] This category contains the data extracted from online platforms providing different courses such as Coursera, edX, and research websites such as ResearchGate, LinkedIn, etc.…”

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

confidence: 99%

“…From the related reviewed articles, we observed that very few studies employed word embedding techniques to represent textual data collected from different sources. Only one article [48] employed the Word2Vec embedding model to learn the numeric representation and supply it as an input to the long short-term memory (LSTM) network. In addition to Word2Vec, GloVe and FastText models were used in two articles [14,45] to generate the embeddings for an input layer of CNN and compare the performance of the proposed aspect-based opinion mining system.…”

Section: Rq7 What Are the Most Common Data Representation Techniques Used For Sentiment Analysis?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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“…Several studies utilized sentiment analysis in different ways. Multilingual student comments, obtained through student feedback, were used to evaluate online courses' effectiveness and teachers' performance in [3,[15][16][17]. In [3], the dataset was collected using approximately 4000 student comments through surveys conducted on 25 university courses to evaluate the performance of a professor who had been teaching for 10 years, while the sentiment analysis was directed including positive, negative, and eight more emotions.…”

Section: Related Workmentioning

confidence: 99%

“…The performance improved when using the softmax activation function, reaching 89%, 99%, and 90% during training, testing, and validation, respectively. Deep learning was applied on a course evaluation dataset with 3000 student comments using three predefined classes in [17], while the results showed that relu and softmax performed better.…”

Section: Related Workmentioning

confidence: 99%

Arabic Sentiment Analysis on Chewing Khat Leaves using Machine Learning and Ensemble Methods

Yafooz

Hizam

Alromema

2021

Eng. Technol. Appl. Sci. Res.

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Sentiment analysis plays an important role in obtaining speakers' opinions or feelings towards events, products, topics, or services, helping businesses to improve their products. Moreover, governments and organizations investigate and solve current social issues by analyzing perspectives and feelings. This study evaluated the habit of chewing Khat (qat) leaves among the Yemeni society. Chewing Khat plant leaves, is a common habit in Yemen and East Africa. This paper proposes a model to detect information about the Khat chewing habit, how people explore it, and the preference for Khat leaves among Arabic people. A dataset consisting of user comments on 18 youtube videos was prepared through several natural language processing techniques. Several experiments were conducted using six machine learning classifiers and four ensemble methods. Support Vector Machine and Linear Regression had almost 80% accuracy, whereas xgboot was the most accurate ensemble method reaching 77%.

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Performance Analysis of Hyperparameters on a Sentiment Analysis Model

Cited by 12 publications

References 20 publications

Sentiment Analysis of Public Tweets Towards the Emergence of SARS-CoV-2 Omicron Variant: A Social Media Analytics Framework

Sentiment Analysis of Public Tweets Towards the Emergence of SARS-CoV-2 Omicron Variant: A Social Media Analytics Framework

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

Arabic Sentiment Analysis on Chewing Khat Leaves using Machine Learning and Ensemble Methods

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