[Objective] This paper proposes a complete and systematic framework to analyze qualifications from online job postings. It then examines the requirements of Internet-related jobs with the framework. [Methods] First, we retrieved recruitment advertisements for the Internet industry. Then, we constructed an LDA model for topic mining and classification of job descriptions. Finally, we used the Word2Vec model and dependency syntax analysis to obtain the topic-word and degree-word lists to construct the topic ontology. [Results] The empirical analysis revealed the status quo of the Internet industry positions, such as the regional and category distributions, as well as the required qualification for different types of positions. [Limitations] There were few data samples for campus recruitment, which led to deviations between the analysis results and the actual situation. The word-segmentation is not perfect for the LDA model, and some topics were not representative. [Conclusions] The proposed framework could effectively analyze job postings.
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