情绪分析
计算机科学
二元曲线
朴素贝叶斯分类器
人工智能
机器学习
支持向量机
判决
监督学习
感知器
自然语言处理
在线学习
期限(时间)
边距(机器学习)
人工神经网络
三元曲线
万维网
量子力学
物理
作者
Anuj Kumar Singh,Sandeep Kumar,Shashi Bhushan,Pramod Kumar,Arun Vashishtha
出处
期刊:Ingénierie Des Systèmes D'information
[International Information and Engineering Technology Association]
日期:2021-10-31
卷期号:26 (5): 501-506
被引量:3
摘要
When anyone is looking to enroll for a freely available online course so the first and famous name comes in front of the searcher is MOOC courses. So here in this article our focus is to collect the comments by enrolled users for the specified MOOC course and apply sentiment analysis over that data. The significance of our article is to introduce a proficient sentiment analysis algorithm with high perceptive execution in MOOC courses, by seeking after the standards of gathering various supervised learning methods where the performance of various supervised machine learning algorithms in performing sentiment analysis of MOOC data. Some research questions have been addressed on sentiment analysis of MOOC data. For the assessment task, we have investigated a large no of MOOC courses, with the different Supervised Learning methods and calculated accuracy of the data by using parameters such as Precision, Recall and F1 Score. From the results we can conclude that when the bigram model was applied to the logistic regression, the Multilayer Perceptron (MLP) overcomes the accuracy by other algorithms as SVM, Naive Bayes and achieved an accuracy of 92.44 percent. To determine the sentiment polarity of a sentence, the suggested method use term frequency (No of Positive, Negative terms in the text) to calculate the sentiment polarity of the text. We use a logistic regression Function to predict the sentiment classification accuracy of positive and negative comments from the data.
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