Research Article
Research on Data Analysis and Visualization of Recruitment Positions Based on Text Mining
| | LDA modeling using the Gensim algorithm | | | Input: job description text set () | | | Output: topic inference | | (1) | function Gensim(texts) | | (2) | create part of speech table flags, stop word table stop words | | (3) | use the Jieba library to segment and filter | | (4) | words_ls ← [] | | (5) | for text in texts: | | (6) | words ← remove_top words([w.word for in jp.cut(text)]) | | (7) | words_ls.append(words) | | (8) | end for | | (9) | dictionary ← corpora.Dictionary (word_ls) | | (10) | corpus ← [dictionary.doc2bow (words) for words in words_ls] | | (11) | LDA ← models.ldamodel.LdaModel(corpus= corpus, id2word= dictionary, num_opics= 1) | | (12) | show the top 30 words in each topic | | (13) | for topic in lda.print_topics (num_words= 30): | | (14) | print topic | | (15) | end for | | (16) | end function |
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