A new method for ranking the usefulness of negative online reviews based on combined weighting method and improved TOPSIS

Online shopping has gradually become an important way of consumption, and consumers are paying more and more attention to negative reviews. In order to avoid the massive amount of negative review information leading to loss of useful information, this paper proposes a method for evaluating the usefulness of negative online reviews. Firstly, the method constructs an evaluation index system for the usefulness of negative online reviews from three aspects: the form feature, text feature, and reviewer feature of negative reviews, and uses a combination weighting method based on fuzzy analytic hierarchy process (FAHP) and entropy method to determine the weight of each index. Secondly, the usefulness ranking results of negative online reviews are obtained through the improved TOPSIS method based on the combined weighting method. Finally, the empirical analysis of the proposed model is carried out by crawling the negative online reviews of JD.com Fresh Food platform, and the improved model is compared with the traditional TOPSIS model, which proves the feasibility and effectiveness of the model.

Question Text Classification Method of Tourism Based on Deep Learning Model

The Internet of Things applications are diverse in nature, and a key aspect of it is multimedia sensors and devices. These IoT multimedia devices form the Internet of Multimedia Things (IoMT). Compared with the Internet of Things, it generates a large amount of text data with different characteristics and requirements. Aiming at the problems that machine learning and single structure deep learning model cannot effectively grasp the text emotional information in text processing, resulting in poor classification effect, this paper proposes a text classification method of tourism questions based on deep learning model. First, the corpus is trained with word2vec tool based on continuous word bag model to obtain the text word vector representation. Then, the attention mechanism is introduced into the long-short term network (LSTM), and the attention-based LSTM model is constructed for text feature extraction, which highlights the impact of different words in the input text on the text emotion category. Finally, the text features are input into the Softmax classifier to obtain the probability distribution of text categories, and the model is trained combined with the cross entropy loss function. The experimental results show that the average accuracy, recall, and F value are 0.943, 0.867, and 0.903, respectively, which has better classification effect than other methods.

Automatic Translation of Medical Information Based on Text Feature Recognition

In order to improve the effect of the medical information automatic translation system, based on the text feature recognition technology of medical image, this paper constructs an automatic translation system that can recognize medical images. On the basis of the medical information image retrieval based on the combination of text information and visual information, this paper uses automatic image annotation technology and semantic similarity calculation method to extract the text and semantic features of medical information images. Then, this paper uses the inherent multi-information fusion capability of the Bayesian inference network to fuse the text features of medical information images and the semantic features of image content together to realize medical information image retrieval. Finally, this paper designs experiments to test the performance of the medical information automatic translation system. The research shows that the system constructed in this paper has certain effects.

A Text-Driven Aircraft Fault Diagnosis Model Based on Word2vec and Stacking Ensemble Learning

Traditional aircraft maintenance support work is mainly based on structured data. Unstructured data, such as text data, have not been fully used, which means there is a waste of resources. These unstructured data contain a great storehouse of fault knowledge, which could provide decision support for aircraft maintenance support work. Therefore, a text-based fault diagnosis model is proposed in this paper. The proposed method uses Word2vec to map text words into vector space, and the extracted text feature vectors are then input into the classifier based on a stacking ensemble learning scheme. Its performance has been validated using a real aircraft fault text dataset. The results show that the fault diagnosis accuracy of the proposed method is 97.35%, which is about 2% higher than that of the suboptimal method.

Research on Semantic Similarity of Short Text Based on Bert and Time Warping Distance

The research on semantic similarity of short text plays an important role in machine translation, emotion analysis, information retrieval and other AI business applications. However, according to existing short text similarity research, the characteristics of ambiguous vocabularies are difficult to be effectively analyzed, the solution of the problem caused by words order needs to be further optimized as well. This paper proposes a short text semantic similarity calculation method that combines BERT and time warping distance algorithm, in order to solve the problem of vocabulary ambiguity. The model first uses the pre trained Bert model to extract the semantic features of the short text from the whole level, and obtains a 768 dimensional short text feature vector. Then, it transforms the extracted feature vector into a point sequence in space, uses the CTW algorithm to calculate the time warping distance between the curves connected by the point sequence, and finally uses the weight function designed by the analysis, according to the smaller the time warpage distance is, the higher the degree of small similarity is, to calculate the similarity between short texts. The experimental results show that this model can mine the feature information of ambiguous words, and calculate the similarity of short texts with lexical ambiguity effectively. Compared with other models, it can distinguish the semantic features of ambiguous words more accurately.

An Unmanned Aerial Vehicle Troubleshooting Mode Selection Method Based on SIF-SVM with Fault Phenomena Text Record

At present, the research on fault analysis based on text data focuses on fault diagnosis and classification, but it rarely suggests how to use that information to troubleshoot faults reported in unmanned aerial vehicles (UAVs). Selecting the exact troubleshooting procedure to address faults reported by UAVs generally requires experienced technicians with professional equipment. To improve the efficiency of UAV troubleshooting, this paper proposed a troubleshooting mode selection method based on SIF-SVM (Serial information fusion and support vector machine) using the text feature data from fault description records. First, Word2Vec was used in text data feature extraction. Second, in order to increase the amount of information in the modeling data, we used the information fusion method. SVM was then used to construct the classification model for troubleshooting mode selection. Finally, the effectiveness of the proposed model was verified by using the fault record data of a new fixed-wing UAV.

Research on Extraction of Useful Tourism Online Reviews Based on Multimodal Feature Fusion

To effectively identify the influencing factors of the perceived usefulness of multimodal data in online reviews of tourism products, this article explores the optimization method of online tourism products based on user-generated content and conducts feature fusion of multimodal data in online reviews of tourism products from the perspective of data fusion analysis. Therefore, based on the word vector model, this article proposes a method to select the seed word set of emotion dictionary. In this method, emotional words are represented in vector form and the distance between word vectors is calculated to form the selection criteria and classification basis of seed word set, and then the sentiment dictionary of online review is formed by category judgment. This article takes the real online review data of tourism products as the research object, carries out descriptive statistical analysis, uses machine learning and deep learning methods, carries out text vector embedding and image content recognition, integrates image and text feature vector, constructs multimodal online review usefulness classification model, and conducts model test. The experimental results show that, compared with the single-mode reviews containing only text or pictures, the multimodal reviews combined with text and pictures can better predict the usefulness of online reviews, improve the quality of online reviews, give full play to the potential value of user-generated content, provide optimization ideas for product providers, and provide decision support for product consumers.