Quinoa starch microparticles (QSMs) fabricated with unhydrolyzed quinoa starch (QS) by inverse microemulsion technology could be used as medicine delivery and cosmetic accessories. The size distribution of QSMs was uniform at 28.5 μM, and the adsorption capacity of methylene blue was 0.82 mg/ g. The optimum preparation process was as follows: a QS mass fraction of 9%, an epoxy chloropropane content of 1%, an amount of oil phase of 100 mL, a span 80 of 3 mg/mL, and a stirring speed of 400 r/min for 3 h at room temperature and pressure. The QSMs were spherical, ellipsoidal in shape with the specific surface area of 1.676 m 2 /g. The crystallinity of the QSMs increased from 1.24% to 16.8% after crosslinking. The maximum thermal degradation temperature of the QSMs rose slightly than QS. It will be a promising material for application as a drug carrier and in cosmetics.

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The Product Sensitive Online Dirichlet Allocation model (PSOLDA) proposed in this paper mainly uses the sentiment polarity of topic words in the review text to improve the accuracy of topic evolution. First, we use Latent Dirichlet Allocation (LDA) to obtain the distribution of topic words in the current time window. Second, the word2vec word vector is used as auxiliary information to determine the sentiment polarity and obtain the sentiment polarity distribution of the current topic. Finally, the sentiment polarity changes of the topics in the previous and next time window are mapped to the sentiment factors, and the distribution of topic words in the next time window is controlled through them. The experimental results show that the PSOLDA model decreases the probability distribution by 0.160 1, while Online Twitter LDA only increases by 0.069 9. The topic evolution method that integrates the sentimental information of topic words proposed in this paper is better than the traditional model.