<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the rapid development of the Internet, a large number of travelers have posted comments on the experience of tourist attractions on the Internet, but the large number of comments generates a large amount of data, which makes it difficult to understand the experience of tourists. For this reason, this paper improves on the basis of the LSTM keyword analysis algorithm. To obtain the attribute words and viewpoint words of implicit states, a neural network is constructed, and the candidate attribute words are refined. Then, four sentiment rules are refined through the memory space of a deep memory network, according to which the sentiment tendency of keywords is analyzed. Synthesize based on the keyword extraction algorithm and keyword sentiment analysis, and propose an algorithm for analyzing tourists’ experience. Finally, the new algorithm is used to analyze the network comments of the Hainan Forest Scenic Area. The results found that the Hainan Forest Scenic Area, with its beautiful forest scenery, gives tourists a pleasant experience. The word frequency ranking of scenic views is 6.8, which is more forward. The cumulative word frequency of “crowded”, “consumption,” and other related items is only 0.559, which is at a lower level, indicating that the shortcomings of this scenic spot lie in the excessive number of tourists and unstandardized consumption items.</p>
</abstract>

With the rapid development of the Internet, a large number of travelers have posted comments on the experience of tourist attractions on the Internet, but the large number of comments generates a large amount of data, which makes it difficult to understand the experience of tourists. For this reason, this paper improves on the basis of the LSTM keyword analysis algorithm. To obtain the attribute words and viewpoint words of implicit states, a neural network is constructed, and the candidate attribute words are refined. Then, four sentiment rules are refined through the memory space of a deep memory network, according to which the sentiment tendency of keywords is analyzed. Synthesize based on the keyword extraction algorithm and keyword sentiment analysis, and propose an algorithm for analyzing tourists’ experience. Finally, the new algorithm is used to analyze the network comments of the Hainan Forest Scenic Area. The results found that the Hainan Forest Scenic Area, with its beautiful forest scenery, gives tourists a pleasant experience. The word frequency ranking of scenic views is 6.8, which is more forward. The cumulative word frequency of “crowded”, “consumption,” and other related items is only 0.559, which is at a lower level, indicating that the shortcomings of this scenic spot lie in the excessive number of tourists and unstandardized consumption items.

A Visitor Experience Perception Method for Hainan Forest Scenic Area Based on Keyword Analysis of Network Comments

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the rapid development of the Internet, a large number of travelers have posted comments on the experience of tourist attractions on the Internet, but the...

A Visitor Experience Perception Method for Hainan Forest Scenic Area Based on Keyword Analysis of Network Comments

Online veröffentlicht: 05. Juli 2024

Seitenbereich: -

Eingereicht: 23. Feb. 2024

Akzeptiert: 21. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1693

Schlüsselwörter
LSTM, Keyword extraction, Sentiment analysis, Tourist experience

© 2024 Que Jingfei., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Visitor Experience Perception Method for Hainan Forest Scenic Area Based on Keyword Analysis of Network Comments

Online veröffentlicht: 05. Juli 2024

Seitenbereich: -

Eingereicht: 23. Feb. 2024

Akzeptiert: 21. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1693

SchlüsselwörterLSTM, Keyword extraction, Sentiment analysis, Tourist experience

© 2024 Que Jingfei., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
LSTM, Keyword extraction, Sentiment analysis, Tourist experience