Question Text Classification Method of Tourism Based on Deep Learning Model

Luo, Wanli; Zhang, Lei

doi:10.1155/2022/4330701

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…A faster, cost-effective approach to question classification using hybrid semantic and lexical features outperforms traditional methods. Intent categorize The reservation in question answering is addressed using a modular approach and LSTM networks for efficient handling of questions [22][23][24][25][26][27][28][29][30].…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Questions from Text Data Using LSTM-Deep Learning Model

Nadir Hussain,

Dr. Sheikh Muhammad Saqib,

Hamza Arif

et al. 2024

VAWKUM trans. comput. sci.

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This paper discusses the importance of detecting questions in textual data for various applications in natural language processing (NLP), such as question answering and chatbot creation. The proposed approach employs long short-term memory (LSTM) models to accurately identify questions by leveraging the sequential nature of language.The paper highlights that LSTM models address challenges like ambiguous language and varying sentence structures. They allow the model to learn from sequential patterns, crucial for understanding the intent behind the text. The preprocessing steps, including tokenization, embedding, and padding, are detailed to prepare the data for training and testing. The study investigates the impact of hyperparameters like hidden layers, hidden states, and optimizer choice on the LSTM algorithm’s performance. In experiments on benchmark datasets, the proposed LSTM-based approach consistently outperforms conventional machine learning models, achieving a remarkable accuracy of 99.25% on the test dataset. The paper concludes by suggesting future directions, including applyingthe approach to other NLP tasks like named entity recognition, sentiment analysis, and text classification. Further optimization for specific datasets or domains is also encouraged. Overall, this research contributes to robust question detection models in NLP, with potential applications in various fields.

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Section: Literature Reviewmentioning

confidence: 99%

Detection of Questions from Text Data Using LSTM-Deep Learning Model

Nadir Hussain,

Dr. Sheikh Muhammad Saqib,

Hamza Arif

et al. 2024

VAWKUM trans. comput. sci.

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“…After the convolution pooling operation, the output of each layer is fused to obtain a richer local feature below. The hidden layer of the BiLSTM layer has 256 neurons ( Luo & Zhang, 2022 ). Due to the possible overfitting of our model with too numerous parameters, the dropout is set to 0.2 in order to prevent the phenomenon of overfitting lines in the CNN and LSTM network layers ( Ghosh et al, 2022 ).…”

Section: Experimental Process and Researchmentioning

confidence: 99%

A sentiment analysis approach for travel-related Chinese online review content

Li,

Zhao

et al. 2023

PeerJ Computer Science

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Using technology for sentiment analysis in the travel industry can extract valuable insights from customer reviews. It can assist businesses in gaining a deeper understanding of their consumers’ emotional tendencies and enhance their services’ caliber. However, travel-related online reviews are rife with colloquialisms, sparse feature dimensions, metaphors, and sarcasm. As a result, traditional semantic representations of word vectors are inaccurate, and single neural network models do not take into account multiple associative features. To address the above issues, we introduce a dual-channel algorithm that integrates convolutional neural networks (CNN) and bi-directional long and short-term memory (BiLSTM) with an attention mechanism (DC-CBLA). First, the model utilizes the pre-trained BERT, a transformer-based model, to extract a dynamic vector representation for each word that corresponds to the current contextual representation. This process enhances the accuracy of the vector semantic representation. Then, BiLSTM is used to capture the global contextual sequence features of the travel text, while CNN is used to capture the richer local semantic information. A hybrid feature network combining CNN and BiLSTM can improve the model’s representation ability. Additionally, the BiLSTM output is feature-weighted using the attention mechanism to enhance the learning of its fundamental features and lessen the influence of noise features on the outcomes. Finally, the Softmax function is used to classify the dual-channel fused features. We conducted an experimental evaluation of two data sets: tourist attractions and tourist hotels. The accuracy of the DC-CBLA model is 95.23% and 89.46%, and that of the F1-score is 97.05% and 93.86%, respectively. The experimental results demonstrate that our proposed DC-CBLA model outperforms other baseline models.

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“…In recent years, tourism has been developing rapidly, and occupied an important position in the tertiary industry, making significant contributions to politics, economy, culture, and ecology [1][2][3][4][5]. The demand for high-quality tourism talents continues to grow, owing to the structural adjustment and upgrading of tourism, and the integration between culture and tourism [6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Application of Knowledge Chain in the Construction of Online Tourism Education Resources

Chen¹,

Wu²

2022

Int. J. Emerg. Technol. Learn.

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Online tourism education occupies an important position in tourism education. Building a knowledge chain can clearly demonstrate the logical relationship between knowledge points of the entire discipline of tourism education, and enable the students to better grasp and understand the relationship between relevant knowledge points through online tourism education. This paper explores the application of knowledge chain in the construction of online tourism education resources (OTERs). Specifically, the associations between knowledge units in OTERs were modeled, the associations between knowledge units were computed, the structure of knowledge chain was optimized, and the OTER knowledge chain was analyzed in details. Through experiments, an example knowledge chain was constructed, and the students’ satisfaction with the application of OTER knowledge chain was evaluated thoroughly.

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Question Text Classification Method of Tourism Based on Deep Learning Model

Cited by 8 publications

References 24 publications

Detection of Questions from Text Data Using LSTM-Deep Learning Model

Detection of Questions from Text Data Using LSTM-Deep Learning Model

A sentiment analysis approach for travel-related Chinese online review content

Application of Knowledge Chain in the Construction of Online Tourism Education Resources

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