Comparative Study on New Quality Productivity Comparative Study on the Focused Hotspots of New Quality Productivity Based on the Hybrid Algorithm of Word2vec and TF-IDF

• 1、Wuhan University of Science and Technology, School of Management, Wuhan 430065

Abstract：[Objective] This study analyzes data related to "new quality productive forces" on academic and social media platforms to explore the discussion focus toward this concept across different platforms. [Methods] Data were cleaned and processed using Pandas and Numpy, followed by keyword extraction via the TF-IDF algorithm. Word cloud diagrams were generated to visually display platform-specific hotspots of interest. Word2Vec models were employed to train word vectors, with t-SNE dimensionality reduction and Matplotlib visualization revealing semantic relationships between "new quality productive forces" and related terms. Sentiment analysis was conducted using a specialized lexicon to classify textual data. [Results] The findings highlight differences in the discourse on "new quality productive forces" between academic and social media platforms in China. [Conclusion] This study innovatively combines Word2Vec and TF-IDF algorithms to analyze "new quality productive forces," offering novel insights into its discussion trends across diverse domains.?????

Keywords： New Quality Productivity TF-IDF Algorithm Word2Vec Modeling Academic platform social Q&A platform.?????