How to optimize jieba word segmentation to improve the keyword extraction effect of scenic spot comments?

Improve Jieba word segmentation accuracy and optimize keyword extraction of scenic spot comments

When using Jieba word segmentation to process scenic spot comment data, the word segmentation effect directly affects the construction of subsequent LDA theme models and keyword extraction. This article discusses how to optimize Jieba word segmentation and improve the accuracy of keyword extraction.

Question description: You hope to use Jieba word segmentation to generate scenic spot comment word clouds and extract topic keywords through the LDA model. However, it was found that there was a deviation in the existing participle results, which affected the theme extraction effect.

Existing code: (The code is omitted here, the same as the original text)

Optimization strategy:

In order to improve the Jieba word segmentation results, improve the accuracy of keyword extraction and the reliability of the theme model, the following strategies are recommended:

1. Custom Dictionary: In order to improve the accuracy of word segmentation, it is recommended to build a custom dictionary containing tourism-related vocabulary. You can collect common vocabulary from the travel-related thesaurus of search engines (such as Baidu and Google), or extract high-frequency phrases from the scenic spot review data set, build a custom dictionary that is more in line with the scenic spot review context, and load it into the Jieba word segmenter. This can effectively identify and divide more keywords related to scenic spots and reduce ambiguity.

2. Refined stop word filtering: The processing of stop word is crucial for keyword extraction. In addition to using the ready-made Chinese stop word library, you can also supplement or adjust the stop word list according to the characteristics of the scenic spot comments. For example, some words that are stop words in ordinary texts (such as "view" and "environment") may be important keywords in scenic area comments, so they need to be handled with caution. You can identify and remove some irrelevant words by analyzing the review data, while retaining words that make sense for the subject analysis.

Through the above optimization, the accuracy of Jieba word segmentation in scenic spot comment data processing can be significantly improved, thereby improving the effectiveness of keyword extraction and LDA theme models, and ultimately generating more accurate word cloud maps and theme analysis results.

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