How to optimize jieba word segmentation by building a customized thesaurus and stop thesaurus to improve the extraction effect of scenic spot comment themes?

Improve the theme extraction of scenic spot comments: Optimizing Jieba word segmentation strategy

When using Jieba for Chinese word segmentation and combining LDA models to extract scenic spot comment topics, the theme extraction accuracy is often affected due to poor word segmentation effect. In view of this problem, this article proposes two optimization strategies: building a custom vocabulary and a discontinuing vocabulary.

The existing code has the problem of insufficient word segmentation accuracy, which leads to inaccurate topic keywords extracted by the LDA model. For improvement, the following methods are recommended:

Strategy One: Build a custom vocabulary

In view of the particularity of scenic spot comments, it is crucial to build a custom thesaurus related to a scenic spot. You can refer to the following steps:

1. Reverse engineering Sogou Travel Dictionary: Analyze Sogou search engine's tourism dictionary (or other large-scale tourism-related dictionary) and extract vocabulary related to scenic spot comments, such as the name of the scenic spot, service type, facility name, etc.
2. Supplementary field vocabulary: Manually supplement the missing words in Sogou vocabulary but frequently appear in scenic spot comments. This requires analyzing a large number of scenic spot review data to identify those keywords that are wrongly divided or unrecognized by the existing thesaurus.
3. Integration and optimization: Integrate extracted and supplemented vocabulary into a custom thesaurus, and deduplicate and standardize to ensure the quality and consistency of the thesaurus.
4. Loading a custom vocabulary: During the Jieba word segmentation process, loading a custom vocabulary, and giving priority to using a custom vocabulary for word segmentation.

Strategy 2: Build a custom stop word library

In addition to custom vocabulary, optimizing the vocabulary is also important.

1. Utilize GitHub open source resources: There are many open source Chinese disabling thesaurus on GitHub, and choose a suitable one as the basis.
2. Supplementary stop words for scenic spot comments: According to the characteristics of scenic spot comments, add some words that appear frequently in scenic spot comments but do not contribute to the theme extraction, such as some tone auxiliary words, colloquial expressions, etc.
3. Simplify the discontinuation database: Avoid the discontinuation database being too large, resulting in the incorrect deletion of important information.

Code improvement suggestions:

Integrate the above custom thesaurus and stop thesaurus into the code and modify the tokenize and delete_stopwords functions:

 import jieba
from gensim import corpora, models
# ... (Other imports)

# Load custom thesaurus jieba.load_userdict("path/to/your/custom_dictionary.txt")

# Load custom stop word library custom_stop_words = set(open("path/to/your/custom_stopwords.txt", encoding='utf-8').read().splitlines())
broadcastVar = spark.sparkContext.broadcast(custom_stop_words)

# ... (The tokenize and delete_stopwords functions are modified to use custom_stop_words)

Through the above two strategies, the accuracy of Jieba word segmentation can be effectively improved, the influence of noise words can be reduced, and the accuracy and effectiveness of the LDA model extracting scenic spot comment topics can be improved. Remember to replace "path/to/your/custom_dictionary.txt" and "path/to/your/custom_stopwords.txt" with the actual paths to your thesaurus and stop the thesaurus. In addition, consider adjusting LDA model parameters such as num_topics and passes for best results.

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