New Education Policy, 2020: Qualitative (Contents) Analysis and Twitter Mining (Sentiment) Analysis

Kaurav, Rahul Pratap Singh; Suresh, Kalyani; Narula, Sumit; Baber, Ruturaj

doi:10.31620/jccc.12.20/02

Cited by 30 publications

(13 citation statements)

References 8 publications

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“…Where 𝐶𝑅 denotes the contribution rate of the first 𝑘 components of the dimensionality-reduced data. The eigenvalue 𝜆 can be obtained by formula (8). To evaluate the sentiment classification performance of the proposed method objectively, the VADER-based, the BosonNLP-based, KNN-based, SVM-based and BERT-based methods are adopted as comparison in the experiment.…”

Section: B Sentiment Classification Resultsmentioning

confidence: 99%

Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis Method

Gui-jun

Zhao

2022

IEEE Access

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With the speedy development of e-commerce, a growing number of customers tend to share their subjective perceptions of the product or service on the Internet. This phenomenon makes the commercial value of online reviews increasingly prominent. In this context, how to gain insights into consumers' perceptions and attitudes from massive comments has become a hot-button topic. Addressing this requirement, this paper developed a fusion sentiment analysis method combining textual analysis techniques with machine learning algorithms, aiming to mine online product experience. The method mainly consists of three steps. Firstly, inspired by the sensitivity of sentiment dictionary to emotional information, we utilize the dictionary to extract sentiment features. Afterward, the SVM algorithm is adopted to identify sentiment polarities of reviews. Based on this, sentiment topics are extracted from reviews through the LDA model. Furthermore, to avoid the omission of emotional information, the dictionary is extended based on semantic similarity. Meanwhile, in this research, the fact that words in reviews have unequal sentiment contribution, which has been neglected in existing studies, is taken into account. Specifically, we introduce the weighting method to measure the sentiment contribution. Finally, the investigation of consumers' reading experiences of online books on Amazon has verified the feasibility and validity of the method. The results demonstrate that the method accurately determines reviews' emotional tendencies and captures elements affecting reading experiences from reviews. Overall, the research provides an effective way to mine online product experience and track customers' demands, thereby strongly supporting future product improvement and marketing strategy optimization.INDEX TERMS e-commerce product experience, fusion method, machine learning, sentiment analysis, sentiment dictionary.

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Section: B Sentiment Classification Resultsmentioning

confidence: 99%

Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis Method

Gui-jun

Zhao

2022

IEEE Access

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“…The policy is a reduction of the number of institutions in the Indian higher education system across three types of higher education institution clusters in order to increase the gross enrolment ratio to 50 percent by 2035, as targeted, and GDP at 6 percent (Kaurav et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Implicit Impact of English Language Pedagogical Enhancement Policies in Higher Education Under the Indian NEP 2020: Challenges, Curriculum, Approaches, Opportunities, and Implementations

Yadav

2023

Am. J. Educ. Technol.

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A country’s societal and economic growth needs well-planned, dedicated, open, and technological advancements in education systems and learning policies (Archer, 2013). Since ancient times, India has dominated education, and university education is widespread there. Before and after independence, India’s education system had many improvements. The current research critically explores the influences of English teaching and learning pedagogies and enhancement strategies in higher education under the Indian National Education Policy (NEP) 2020. The research employed a descriptive-quantitative approach that collected quantifiable information for statistical analysis of the population sample. Moreover, the study uncovers challenges, curriculum, approaches, opportunities, and implementations under the NEP 2020. The study used a questionnaire to randomly collect data from 200 students to investigate the implicit impact of executing English language teaching and learning programs. The impact was evaluated across four categories: high, medium, neutral, and low, and the analysis of the data showed the high impact of implementing the NEP in India. This study is a preliminary analysis of a policy document that will be used as a starting point for future studies using empirical methods to examine the effects of the NEP once it is fully implemented. Focusing on changes in higher education, the paper has elucidated the holistic, transformative understanding of NEP and highlighted the significance of technology interventions for innovative teaching and learning. This article can be considered as a reference to the policy implementation of English language pedagogical enhancement policies in higher education by the NEP 2020 teams of the Government of India.

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“…The policy discussed students’ internships at local industries and businesses to get practical experience as part of learning and improve their employability. The NPE focuses on higher education preparing industry-ready students and encouraging students for entrepreneurship (Kaurav et al, 2020), the creation of a knowledge-driven society by developing required skills and scientific temperament among students (Kumar, 2020). The policy also focussed on research and innovation in higher education institutions by establishing incubation centres, greater linkages between academia and industry, and establishing of technology development centres.…”

Section: Methodsmentioning

confidence: 99%

Trends of Shifting Role of University Education towards University–Industry Relations

Machahary¹,

Upadhyaya

2022

Higher Education for the Future

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The increasing nature of global knowledge has made higher education move to a closer relationship with the industries. Universities in India have been given paramount importance for the country’s development; hence, recommendations were forwarded by various educational commissions and policies to focus on university–industry relations (UIR). The article attempts to understand UIR by highlighting its opportunities and challenges. The present article studied the recommendation of policy documents of higher education in India on UIR in the post-independence period, focusing on the roles of university education. The documents in this article include the reports of various educational commissions, namely the University Education Commission 1948–49, the Education Commission of 1964–66 and the National Knowledge Commission of 2005 and also the documents of significant educational policies of India, namely the National Policy on Education 1968 , National Policy on Education 1986 and National Education Policy 2020. Documents analysis as a method was used to understand the recommendation on UIR. The growing concern of university–industry collaboration can be seen in policy documents.

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New Education Policy, 2020: Qualitative (Contents) Analysis and Twitter Mining (Sentiment) Analysis

Cited by 30 publications

References 8 publications

Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis Method

Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis Method

Implicit Impact of English Language Pedagogical Enhancement Policies in Higher Education Under the Indian NEP 2020: Challenges, Curriculum, Approaches, Opportunities, and Implementations

Trends of Shifting Role of University Education towards University–Industry Relations

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