Recognition and Analysis of Emotional Words in Ancient Chinese Poetry under Different Themes

Wei, Zhang; Wang, Hao

doi:10.1002/pra2.495

Cited by 2 publications

(1 citation statement)

References 7 publications

(6 reference statements)

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“…In Equation ( 13), 𝑐 signifies the cell state at the current time step, while 𝑜 represents the weight proportion associated with the output information [35].…”

Section: B Long Short-term Memory Neural Networkmentioning

confidence: 99%

The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things

Meng

2024

IEEE Access

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The relentless progression of advanced technologies has driven the seamless integration of Internet of Things (IoT) services into the fundamental framework of contemporary tourism enterprises. In the quest for valuable insights from the vast reservoir of tourism data, this study employs a Convolutional Neural Network (CNN) as its primary instrument, culminating in the development of a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model designed for the classification of textual data in the smart tourism domain. The primary function of this model is to conduct sentiment analysis on smart tourism data, specifically by categorizing review data into either positive or negative sentiments. The incorporation of review ratings further aids in the accurate labeling of data according to their respective sentiment categories, thus streamlining the process of effective data annotation. The empirical findings reveal distinctive performance trajectories for the three models concerning positive comments across various evaluation metrics. Remarkably, in terms of precision, the CNN-BiLSTM model leads with an impressive 87.9%, closely followed by the BiLSTM model at 87.4%, while the Text Convolutional Neural Network (TextCNN) model trails slightly at 85.3%. Similarly, the recall highlights the CNN-BiLSTM model's excellence, achieving an impressive 88.34%, compared to 78.5% for BiLSTM and 77.6% for TextCNN models. In the realm of accuracy, the CNN-BiLSTM model maintains its dominance at 85.1%, while the BiLSTM and TextCNN models achieve 82.7% and 81.4%, respectively. Notably, the CNN-BiLSTM model outperforms the trio in terms of the F1 value, securing a robust 85.41%. In summary, the CNN-BiLSTM model consistently demonstrates excellent performance across a range of metrics, including precision, recall, accuracy, and F1 value, signifying its supremacy in this classification task. This study presents a systematic solution for enhancing smart tourism services, thereby providing a strong foundation for the growth and advancement of tourism enterprises.

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“…In Equation ( 13), 𝑐 signifies the cell state at the current time step, while 𝑜 represents the weight proportion associated with the output information [35].…”

Section: B Long Short-term Memory Neural Networkmentioning

confidence: 99%

The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things

Meng

2024

IEEE Access

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Evaluation of rural tourism development level using BERT-enhanced deep learning model and BP algorithm

Yuan

2024

Sci Rep

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To address the insufficient expressive capabilities of traditional methods in assessing the development level of rural tourism, this study explores the fusion application of the Bidirectional Encoder Representations from Transformers (BERT) and the Back Propagation (BP) algorithm to enhance the accuracy and comprehensiveness of rural tourism development assessment. Firstly, this study introduces the BERT deep learning model and its applications in natural language processing, alongside the role of the BP algorithm in pattern recognition and predictive analysis. Subsequently, a framework for assessing rural tourism development levels, integrating BERT and the BP algorithm, is proposed. This framework collects multidimensional rural tourism-related data and utilizes the BERT model for sentiment analysis and topic extraction from textual data. Empirical analysis of rural tourism development in a specific region validates the effectiveness of the proposed approach. Experimental results demonstrate: (1) The model achieves an accuracy of 84.33% and an F1 score of 85.33% on the publicly available Laptop dataset, with a processing time of 20 s, significantly outperforming other methods. Compared to traditional approaches, the proposed method accurately captures correlations between textual information and numerical data, thereby enhancing the credibility and accuracy of assessment results. (2) From the ablation study results, it is evident that removing any component from the model leads to performance degradation. Specifically, removing the Bidirectional Gated Recurrent Unit (BiGRU) reduces accuracy and F1 scores to 78.21% and 76.33% on the Laptop dataset, and to 85.10% and 70.45% on the Tourist_F dataset. Removing Text Convolutional Neural Network (CNN) reduces accuracy and F1 scores to 79.34% and 77.56% on the Laptop dataset, and to 86.25% and 72.11% on the Tourist_F dataset. The most significant performance decline occurs upon removing BERT, with accuracy and F1 scores decreasing to 70.14% and 66.43% on the Laptop dataset, and to 78.45% and 60.33% on the Tourist_F dataset. These results indicate that BiGcRU, TextCNN, and BERT each contribute significantly to the model’s performance. This study provides substantial support for advancing sustainable development in rural tourism, offering significant practical and innovative value.

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Recognition and Analysis of Emotional Words in Ancient Chinese Poetry under Different Themes

Cited by 2 publications

References 7 publications

The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things

The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things

Evaluation of rural tourism development level using BERT-enhanced deep learning model and BP algorithm

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