How to improve the accuracy of jieba word segmentation in scenic spot comment analysis?

Optimize jieba word segmentation to improve the accuracy of scenic spot comment analysis

In the emotional analysis of scenic spot comments, the accuracy of jieba word segmentation directly affects the modeling effect of LDA theme models and the accuracy of keyword extraction, and thus affects the quality of word cloud map. This article proposes an optimization plan for the problems that arise in the review and analysis of jieba word segmentation in scenic spots.

In the code snippets of user feedback, the jieba word segmentation result is not ideal, resulting in the inaccurate subject words extracted by the LDA model. The problem is mainly reflected in word segmentation accuracy and stop word processing.

The following code snippet shows the user-provided code and the existing problems:

 # ... (User-provided code snippet) ...

To solve this problem, we recommend two strategies:

1. Building a custom scenic spot lexicon: Using the lexicon directly with Jieba's own lexicon may not cover the unique lexicon in scenic spot comments (for example, the name of a specific scenic spot, the name of a ride facility, etc.). Building a custom vocabulary containing relevant vocabulary in scenic spots can significantly improve the accuracy of word segmentation. This can be achieved through the following steps:

   • Collect vocabulary of scenic spots: collect vocabulary of scenic spots from tourism websites, APPs, official scenic spot websites and other channels, including scenic spot names, facility names, service types, etc.
   • Construct the vocabulary file: organize the collected vocabulary into a text file with one word per line.
   • Loading a custom vocabulary: Use the jieba.load_userdict() function to load a custom vocabulary, so that jieba will prioritize the use of vocabulary in the custom vocabulary when participling words.

2. Optimizing stop word processing: The processing of stop word is also crucial. stopwords.words('chinese') is used in the user code, but the lexicon may not be comprehensive enough to cover all meaningless words in scenic spot comments. suggestion:

   • Use a more comprehensive stop-on-use thesaurus: Obtain a more complete Chinese stop-on-use thesaurus from platforms such as GitHub, and adjust and supplement according to actual conditions.
   • Custom stop words: According to the characteristics of scenic spot comments, add some common words in scenic spot comments but meaningless analysis of themes to the stop word library.

Through the optimization of the above two aspects, the accuracy of jieba word segmentation in scenic spot comment analysis can be significantly improved, thereby obtaining more accurate topic words and keywords, improving the quality of word cloud maps and the reliability of the overall analysis results. It is recommended that users re-run the LDA model after building a custom vocabulary and a disable vocabulary, compare the results and verify the optimization effect.

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