The review text received by crowdsourcing participants contains valuable knowledge, opinions, and preferences, which is an important basis for employers to make trading decisions, and crowdsourcing participants to improve service level and quality. However, there are two kinds of emotional polarity in the review text, the attention paid to sentiment classification of review text with fuzzy emotional boundaries is insufficient. This paper proposes a supervised text sentiment classification method with Latent Dirichlet Allocation (LDA) to improve the classification performance of review text with fuzzy sentiment boundaries. Taking the review text of crowdsourcing participants on the Zhubajie platform as the data set, using N-gram, Word2vec, and TF-IDF algorithms to extract text features. The LDA topic model is applied to expand the number of text features and extract eight topics that affect employers’ sentiment tendencies. Text classifiers are constructed based on Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GDBT), and Extreme Gradient Boosting (XGBoost) algorithms, and the effectiveness of the sentiment classification methods are verified by ten-fold cross-validation and confusion matrix. Experimental results show that using the LDA topic model to extend the features of review text can effectively alleviate the problem that the classifier is difficult to distinguish the sentiment categories of different emotion polarity words coexisting text, and enhance the ability of emotion boundary fuzzy text classification. Based on TF-IDF and LDA to extract and expand text features, the GBDT text sentiment classifier with the accuracy of 0.881; the F1-measure of the second, third, fourth, and fifth categories samples are 0.462, 0.571, 0.278, and 0.647 respectively, which is better than SVM, RF, and XGBoost classifiers and has the best classification performance.

Building a scientific and reasonable reputation evaluation mechanism for crowdsourcing participants is an effective way to solve the problem of transaction fraud, to establish the trust of traders and ensure the quality of task completion. Under the big data environment, machine learning methods have been applied in the domain of e-commerce of physical goods to improve the traditional reputation evaluation methods, and achieved good results. However, few studies have applied machine learning methods to crowdsourcing, a form of service e-commerce, to evaluate the reputation of participants. This paper proposes a reputation evaluation model (i.e. LDA-RF) for crowdsourcing participants of Random Forest based on Linear Discriminant Analysis. The model consists of five steps: firstly, building a multidimensional reputation evaluation index system for crowdsourcing participants, collecting real data sets, and preprocessing data; secondly, data dimensionality reduction methods, including Linear Discriminant Analysis, Principal Component Analysis, Mean Impact Value method and ReliefF feature selection method, are used to eliminate redundant variables; thirdly, data normalization; fourthly, with selected feature subset, five machine learning techniques, Random Forest, Decision Tree, Back propagation Neural Network, Radial Basis Function Neural Network and Support Vector Machine are used to train the model; Fifthly, the validity of the model is tested by four evaluation measures: 10 fold cross validation, confusion matrix, Kruskal-wallis test and dispersion degree. The results show that the LDA-RF model on accuracy, F1-measure, generalization ability and robustness are better than those of other models, and it has better performance and effectiveness. This study represents a new contribution to establish reputation evaluation of crowdsourcing participants under big data environment.

With the rise of global smart city construction, target detection technology plays a crucial role in optimizing urban functions and improving the quality of life. However, existing target detection technologies still have shortcomings in terms of accuracy, real-time performance, and adaptability. To address this challenge, this study proposes an innovative target detection model. Our model adopts the structure of YOLOv8-DSAF, comprising three key modules: depthwise separable convolution (DSConv), dual-path attention gate module (DPAG), and feature enhancement module (FEM). Firstly, DSConv technology optimizes computational complexity, enabling real-time target detection within limited hardware resources. Secondly, the DPAG module introduces a dual-channel attention mechanism, allowing the model to selectively focus on crucial areas, thereby improving detection accuracy in high-dynamic traffic scenarios. Finally, the FEM module highlights crucial features to prevent their loss, further enhancing detection accuracy. Additionally, we propose an Internet of Things smart city framework consisting of four main layers: the application domain, the Internet of Things infrastructure layer, the edge layer, and the cloud layer. The proposed algorithm utilizes the Internet of Things infrastructure layer, edge layer, and cloud layer to collect and process data in real-time, achieving faster response times. Experimental results on the KITTI V and Cityscapes datasets indicate that our model outperforms the YOLOv8 model. This suggests that in complex urban traffic scenarios, our model exhibits superior performance with higher detection accuracy and adaptability. We believe that this innovative model will significantly propel the development of smart cities and advance target detection technology.

In the era of big data, the online ordering form of “Internet + traditional catering” has adapted to the needs of consumers with a fast pace of life and personalized consumption mode and is booming all over the world. However, due to the consumer information asymmetry and the lack of effective supervision, the potential food safety problems are becoming increasingly prominent. This paper comprehensively uses the social network analysis and Latent Dirichlet Allocation method to mine the text data of consumer comments on the online ordering platform and puts forward five food safety problems existing in the online ordering platform. Then, text features are extracted by using Bert, TF‐IDF, Word2vec, and N‐gram algorithms, and classifiers based on GBDT, XGBoost, LSTM, BiLSTM, CNN, RNN, and CRNN algorithms are cross constructed to identify text reviews with potential food safety hazards. The classifier’s performance is compared and evaluated through ten‐fold cross‐validation, Friedman test, and confusion matrix. The research results show that the BERT‐GBDT classifier has the best performance in accuracy, precision, specificity, and F1 measure value, and stability is the strongest. It has the best distinguish effect on the text of the review with potential food safety hazards.