How to improve the accuracy of jieba word segmentation in scenic spot comment word cloud maps by building a custom vocabulary and optimizing stop word processing?

Accurate word segmentation to create a clearer cloud of comments in scenic spots

When using jieba word segmentation to generate scenic spot comment word clouds, accurate word segmentation is crucial. This article provides optimization solutions to improve the accuracy of word cloud maps for word segmentation problems in LDA subject word extraction feedback.

The code snippet provided by the user shows steps such as jieba word segmentation, stop word filtering, and punctuation removal. However, the default jieba word segmentation and stop word library may not fully meet the special context of scenic spot comments.

To optimize word segmentation results, the following strategies are recommended:

1. Building a special thesaurus for scenic spot comments: Make full use of existing resources, such as Sogou Tourism Thesaurus, and combine the characteristics of scenic spot comment texts to build a more accurate custom thesaurus. A custom vocabulary should contain professional terms, common vocabulary and phrases related to scenic spots, such as scenic spot names, facility names, service types, etc., to improve the ability of Jieba word segmentation to recognize specific vocabulary in scenic spot comments.

2. Customized stop word processing: Open source stop word library based on platforms such as github, and combined with the characteristics of scenic spot comment texts, create a more suitable stop word library. For example, some words that are stop words in ordinary texts (such as "天", "天", "天") may contain important information in scenic spot comments and need to be handled with caution. On the contrary, words that appear frequently in comments in scenic spots but have little meaning should be added to the discontinuing vocabulary.

By building a custom vocabulary and optimizing stop word processing, the error of jieba word segmentation can be effectively reduced, the accuracy of lda topic word extraction can be improved, and ultimately a clearer and more accurate scenic spot comment word cloud map can be generated. This will help to more effectively analyze tourist evaluations and provide more reliable data support for scenic spot management and improvement.

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