Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of Things
This work aims to introduce Long Short-Term Memory (LSTM) under the Internet of Things (IoT) context to enhance the accuracy and granularity of sentiment analysis in animated online education texts. It employs a multimodal data collection approach and uses IoT technology to gather multimodal textual...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10268925/ |
| _version_ | 1797661503378161664 |
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| author | Jun Mao Zhe Qian Terry Lucas |
| author_facet | Jun Mao Zhe Qian Terry Lucas |
| author_sort | Jun Mao |
| collection | DOAJ |
| description | This work aims to introduce Long Short-Term Memory (LSTM) under the Internet of Things (IoT) context to enhance the accuracy and granularity of sentiment analysis in animated online education texts. It employs a multimodal data collection approach and uses IoT technology to gather multimodal textual data from students engaged in animated online education. The data includes students’ feedback texts, emotional texts, written texts, and verbal expressions during animated online education. Subsequently, a model named Information Block Bidirectional Long-Short term Memory (IB-BiLSTM) is designed and utilized to construct a sentiment classification model for animated online education texts. Experimental results demonstrate that the model achieves an accuracy of 93.92% and an F1-score of 90.34% for sentiment classification in animated online education texts and the loss function converges to around 0.14. This model effectively captures the emotional changes and evolution during students’ learning process. Thus, the proposed model holds significant potential and practical significance for enhancing animated online education’s personalization and emotional engagement. It provides valuable insights and guidance for the intelligent development of the education field. |
| first_indexed | 2024-03-11T18:45:29Z |
| format | Article |
| id | doaj.art-3a0bb4e13ce040ae876ab742047fcabe |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T18:45:29Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3a0bb4e13ce040ae876ab742047fcabe2023-10-11T23:00:15ZengIEEEIEEE Access2169-35362023-01-011110912110913010.1109/ACCESS.2023.332130310268925Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of ThingsJun Mao0Zhe Qian1Terry Lucas2https://orcid.org/0000-0003-1954-2427Faculty of Applied and Creative Arts, Universiti Malaysia Sarawak, Kota Samarahan, MalaysiaCollege of Art Design, Hangzhou College of Commerce, Zhejiang Gongshang University, Hangzhou, ChinaFaculty of Applied and Creative Arts, Universiti Malaysia Sarawak, Kota Samarahan, MalaysiaThis work aims to introduce Long Short-Term Memory (LSTM) under the Internet of Things (IoT) context to enhance the accuracy and granularity of sentiment analysis in animated online education texts. It employs a multimodal data collection approach and uses IoT technology to gather multimodal textual data from students engaged in animated online education. The data includes students’ feedback texts, emotional texts, written texts, and verbal expressions during animated online education. Subsequently, a model named Information Block Bidirectional Long-Short term Memory (IB-BiLSTM) is designed and utilized to construct a sentiment classification model for animated online education texts. Experimental results demonstrate that the model achieves an accuracy of 93.92% and an F1-score of 90.34% for sentiment classification in animated online education texts and the loss function converges to around 0.14. This model effectively captures the emotional changes and evolution during students’ learning process. Thus, the proposed model holds significant potential and practical significance for enhancing animated online education’s personalization and emotional engagement. It provides valuable insights and guidance for the intelligent development of the education field.https://ieeexplore.ieee.org/document/10268925/The Internet of Thingslong short-term memory networkstext sentiment analysisanimation online educationmultimodal data |
| spellingShingle | Jun Mao Zhe Qian Terry Lucas Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of Things IEEE Access The Internet of Things long short-term memory networks text sentiment analysis animation online education multimodal data |
| title | Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of Things |
| title_full | Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of Things |
| title_fullStr | Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of Things |
| title_full_unstemmed | Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of Things |
| title_short | Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of Things |
| title_sort | sentiment analysis of animated online education texts using long short term memory networks in the context of the internet of things |
| topic | The Internet of Things long short-term memory networks text sentiment analysis animation online education multimodal data |
| url | https://ieeexplore.ieee.org/document/10268925/ |
| work_keys_str_mv | AT junmao sentimentanalysisofanimatedonlineeducationtextsusinglongshorttermmemorynetworksinthecontextoftheinternetofthings AT zheqian sentimentanalysisofanimatedonlineeducationtextsusinglongshorttermmemorynetworksinthecontextoftheinternetofthings AT terrylucas sentimentanalysisofanimatedonlineeducationtextsusinglongshorttermmemorynetworksinthecontextoftheinternetofthings |