Opinion mining technique for developing student feedback analysis system using lexicon-based approach (OMFeedback)

Wook, Muslihah; Razali, Noor Afiza Mat; Ramli, Suzaimah; Wahab, Norshahriah Abdul; Hasbullah, Nor Asiakin; Zainudin, Norulzahrah Mohd; Talib, Muhamad Lazim

doi:10.1007/s10639-019-10073-7

Cited by 20 publications

(12 citation statements)

References 11 publications

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“…Sentiment analysis (or opinion mining) is a Natural Language Processing (NLP) approach to investigate the emotions, polarity, attitude, and opinion of the people from the written text (Liu & Zhang, 2012;Wook et al, 2019). In our survey, we have taken the feedbacks (suggestions or comments) from the respondents expressing their experience with the online conduct of the classes (online teaching and learning).…”

Section: Sentiment Analysismentioning

confidence: 99%

Indian government E-learning initiatives in response to COVID-19 crisis: A case study on online learning in Indian higher education system

et al. 2021

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Recently, the whole world has faced the deadliest and dangerous consequences due to the transmission of infectious novel coronavirus (nCov). With the outbreak of COVID-19, the education learners, practitioners, and other stakeholders were at the sake of a loss, as it causes the suspension of physical classes and physical interaction of the learners. In these circumstances, Electronic learning (E-learning), Online learning, and the use of Information and Communications Technology (ICT) tools came in handy. It helped the learners in the dissemination of ideas, conducting online classes, making online discussion forums, and taking online examinations. Like the government of each country, the Indian government was also caught off-guard but the existing E-learning infrastructure was able to leverage on while devising plans to tailor them to new situations and launching new ones. The initiatives at the forefront of this noble battle launched by the Department of School Education and Literacy, Ministry of Human Resources Development (MHRD) includes Diksha, Swayam Prabha Channel, Shiksha Van, E-Pathshala, and National Repository of Open Educational Resources (NROER). It worth noting that apart from the Indian central government efforts, each state has various online education initiatives that are tailored to their needs. This research evaluated each of these initiatives commenced by central and state governments and present a detailed analysis of most of the relevant initiatives. Additionally, a survey is conducted to get insights of learners in concern to online learning. Despite the issues raised in this learning, the outcomes come to be satisfactorily favoring online learning.

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Section: Sentiment Analysismentioning

confidence: 99%

Indian government E-learning initiatives in response to COVID-19 crisis: A case study on online learning in Indian higher education system

et al. 2021

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“…In this study, a comparative analysis was conducted employing VADER (Valence Aware Dictionary and sEntiment Reasoner), a lexicon-based sentiment analysis tool developed specifically for social media content, such as tweets (Wook et al, 2019 ). VADER is a rule-based system that evaluates sentiment using a predefined lexicon of words and phrases, each with assigned polarity scores.…”

Section: Methodsmentioning

confidence: 99%

“…In the end, VADER returns the sentiment score for the tweet, along with information regarding sentiment strength and classification as positive, negative, or neutral. Furthermore, VADER proves to be a quick and efficient approach for analyzing sentiment in Twitter data, making it a valuable tool for rapidly assessing the overall sentiment of a large volume of tweets related to a specific topic or event (Wook et al, 2019 ). In reference to a study in Hutto and Gilbert ( 2014 ), the authors investigated the classification accuracy, employing thresholds set at −0.05 and +0.05 for all normalized sentiment scores ranging between −1 and 1.…”

Section: Methodsmentioning

confidence: 99%

Sentiment analysis of COP9-related tweets: a comparative study of pre-trained models and traditional techniques

Elmitwalli,

Mehegan

2024

Front. Big Data

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IntroductionSentiment analysis has become a crucial area of research in natural language processing in recent years. The study aims to compare the performance of various sentiment analysis techniques, including lexicon-based, machine learning, Bi-LSTM, BERT, and GPT-3 approaches, using two commonly used datasets, IMDB reviews and Sentiment140. The objective is to identify the best-performing technique for an exemplar dataset, tweets associated with the WHO Framework Convention on Tobacco Control Ninth Conference of the Parties in 2021 (COP9).MethodsA two-stage evaluation was conducted. In the first stage, various techniques were compared on standard sentiment analysis datasets using standard evaluation metrics such as accuracy, F1-score, and precision. In the second stage, the best-performing techniques from the first stage were applied to partially annotated COP9 conference-related tweets.ResultsIn the first stage, BERT achieved the highest F1-scores (0.9380 for IMDB and 0.8114 for Sentiment 140), followed by GPT-3 (0.9119 and 0.7913) and Bi-LSTM (0.8971 and 0.7778). In the second stage, GPT-3 performed the best for sentiment analysis on partially annotated COP9 conference-related tweets, with an F1-score of 0.8812.DiscussionThe study demonstrates the effectiveness of pre-trained models like BERT and GPT-3 for sentiment analysis tasks, outperforming traditional techniques on standard datasets. Moreover, the better performance of GPT-3 on the partially annotated COP9 tweets highlights its ability to generalize well to domain-specific data with limited annotations. This provides researchers and practitioners with a viable option of using pre-trained models for sentiment analysis in scenarios with limited or no annotated data across different domains.

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“…Ref. [4] suggests utilizing OMFeedback, a specially designed software system, to gather and analyze student input using a lexicon-based method and the Vader Sentiment Intensity Analyzer. Ref.…”

Section: Related Workmentioning

confidence: 99%

Supervised Sentiment Analysis of Indirect Qualitative Student Feedback for Unbiased Opinion Mining

Prasad,

Nakka

2023

RAiSE-2023

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Opinion mining technique for developing student feedback analysis system using lexicon-based approach (OMFeedback)

Cited by 20 publications

References 11 publications

Indian government E-learning initiatives in response to COVID-19 crisis: A case study on online learning in Indian higher education system

Indian government E-learning initiatives in response to COVID-19 crisis: A case study on online learning in Indian higher education system

Sentiment analysis of COP9-related tweets: a comparative study of pre-trained models and traditional techniques

Supervised Sentiment Analysis of Indirect Qualitative Student Feedback for Unbiased Opinion Mining

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