Sentiment analysis of social media data based on chaotic coyote optimization algorithm based time weight‐<scp>AdaBoost</scp> support vector machine approach

Dangi, Dharmendra; Bhagat, Amit; Dixit, Dheeraj Kumar

doi:10.1002/cpe.6581

Cited by 16 publications

(4 citation statements)

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“…There is a keyword named "performance sentiment" in the C4 community. Precision, recall, accuracy and F1-score are the most commonly used evaluation metrics (Dangi et al 2022;Jain et al 2022;JayaLakshmi and Kishore 2022;Li et al 2017;Wang et al 2021;Yi and Niblack 2005). Some researchers have also used runtimes to calculate the model efficiency (Abo et al 2021;Ferilli et al 2015), p-value to statistically evaluate the relationship or difference between two samples of classification results (JayaLakshmi and Kishore 2022; Salur and Aydin 2020), paired sample t-tests to verify that the results are not obtained by chance (Nhlabano and Lutu 2018)…”

Section: Analysis On Research Methods and Topics Of The C4 Communitymentioning

confidence: 99%

Survey on sentiment analysis: evolution of research methods and topics

Cui

Wang

et al. 2023

Artif Intell Rev

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Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword cooccurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work.

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Section: Analysis On Research Methods and Topics Of The C4 Communitymentioning

confidence: 99%

Survey on sentiment analysis: evolution of research methods and topics

Cui

Wang

et al. 2023

Artif Intell Rev

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“…By introducing a more flexible model structure and advanced feature extraction techniques, it is expected to overcome the challenges of traditional SVM in handling digital presentation tasks, thus improving the applicability and performance of the algorithm (Dangi et al, 2021). This is the motivation for working on improving SVM algorithms.…”

Section: Related Workmentioning

confidence: 99%

Visual Design of Digital Display Based on Virtual Reality Technology with Improved SVM Algorithm

Zuo

2024

ICST Transactions on Scalable Information Systems

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NTRODUCTION: With the rapid development of virtual reality (VR) technology, digital displays have become increasingly important in various fields. This study aims to improve the application of virtual reality technology in the visual design of digital displays by improving the support vector machine (SVM) algorithm. The visual design of digital displays is crucial for attracting users, enhancing experience and conveying information, so an accurate and reliable algorithm is needed to support relevant decisions. OBJECTIVES: The purpose of this study is to improve the SVM algorithm to more accurately identify features related to the visual design of digital displays. By exploiting the nonlinear mapping and parameter optimization of the SVM algorithm, it aims to improve the performance of the model so that it can better adapt to complex visual design scenarios. METHODS: In the process of achieving the objective, multimedia data related to digital displays, including images and videos, were first collected. Through feature engineering, features closely related to visual design were selected, and deep learning techniques were applied to extract higher-level feature representations. Subsequently, the SVM algorithm was improved to use the kernel function for nonlinear mapping, and the penalty parameters and the parameters of the kernel function were adjusted. Cross-validation was used in the training and testing phases of the model to ensure its generalization performance. RESULTS: The improved SVM algorithm demonstrated higher accuracy, recall and precision compared to the traditional method by evaluating it on the test set. This suggests that the model is able to capture visual design features in digital displays more accurately and provide more reliable support for relevant decisions. CONCLUSION: This study demonstrates that by improving the SVM algorithm, more accurate visual design can be achieved in digital displays of virtual reality technology. This improvement provides reliable algorithmic support for the design of digital displays and provides a more prosperous, immersive experience for users. Future research can further optimize the algorithm and iterate with user feedback to continuously improve the visual design of digital displays in virtual reality environments.

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“…Social media content merupakan istilah luas yang mencakup berbagai aplikasi berbasis internet yang memungkinkan konten buatan pengguna untuk dipertukarkan dan konsumerisme di Internet, seperti situs jejaring sosial (misalnya, Facebook), situs blogging mikro (misalnya, Twitter), dan situs berbagi foto/ video (misalnya, Tiktok dan Instagram) (Dangi et al, 2022;Jain et al, 2022;Pawar & Jose, 2022).…”

Section: Kajian Teori Social Media Contentunclassified

Social Media Content and Re-Purchashing Behavior: The Mediation Role of E-Wom

Novie

2023

JIMMU

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This research aims to determine the relationship between the role of e- WOM in mediating the relationship between social media content and re-purchasing behavior, while also analyzing the relationship between social media content and e-WOM, and the relationship between e-WOM and re-purchasing behavior. Research using a quantitative approach using partial least squares (PLS). The population in the study were consumers of Deliwafa Store which has branches in Surabaya, Sidoarjo, Gresik, and Malang. The sample in this study was determined using purposive sampling, with a total sample used of 172 respondents. The results of this study indicate that social media content has no significant effect on re-purchasing behavior, social media content has a significant effect on e-WOM, e-WOM has a significant effect on re-purchasing behavior, and e-WOM has a significant effect in mediating the relationship between social media content and re-purchasing behavior.

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Sentiment analysis of social media data based on chaotic coyote optimization algorithm based time weight‐AdaBoost support vector machine approach

Cited by 16 publications

References 46 publications

Survey on sentiment analysis: evolution of research methods and topics

Survey on sentiment analysis: evolution of research methods and topics

Visual Design of Digital Display Based on Virtual Reality Technology with Improved SVM Algorithm

Social Media Content and Re-Purchashing Behavior: The Mediation Role of E-Wom

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