9 results about "Sentiment score" patented technology

This application discloses a method and apparatus for quantifying the quality of rural hotels, as well as a method for processing rural hotel information, belonging to the field of data processing and information technology. The method includes: screening and confirming rural hotels from accommodation facilities within a target area according to preset identification rules to obtain a rural hotel directory; collecting online user review text data for each rural hotel; extracting high-frequency feature words based on the online user review text data, performing semantic analysis, and constructing a quality evaluation index system; mapping each review text to the corresponding index in the quality evaluation index system, and obtaining the global weight of each level of index by statistically analyzing the number of review texts corresponding to the first-level and second-level indicators; performing sentiment analysis on each review text and calculating the sentiment score of each level of index; and calculating the quality quantification score of each rural hotel based on the sentiment score corresponding to each level of index and its corresponding global weight. The embodiments of this application can improve the accuracy of rural hotel quality quantification.

The present application relates to the technical field of data information processing, in particular to a financial text sentiment feature optimization method and system based on multi-supervision signal LoRA fine-tuning. The method comprises: obtaining financial text and transaction data; generating at least three groups of sentiment scores based on a large language model, including original scores, first fine-tuned scores based on financial special model label fine-tuning, and second fine-tuned scores based on future yield label fine-tuning; calibrating each version of the scores and constructing multi-dimensional emotion features; fusing each version of the emotion features with technical index features to form multiple candidate feature sets; using time series cross-validation and multiple heterogeneous evaluation models to systematically evaluate and statistically compare the candidate feature sets, and selecting the optimal emotion feature source according to the performance; and deploying a prediction model based on the optimization result. The present application realizes empirical comparison of fine-tuning strategies and objective optimization of feature sources, can avoid invalid fine-tuning overhead, and has a standardized engineering implementation process.

Embodiments evaluate performance by receiving a plurality of training performance reviews. Embodiments extract from the training performance reviews, using a first machine learning model, a plurality of features comprising a training aspect, a training sentiment, and a corresponding training evidence. Embodiments use the extracted plurality of features to train a second machine learning model. Embodiments receive a first performance review and extract from the first performance review one or more first aspects, one or more corresponding first evidences, and one or more corresponding first sentiments. Embodiments, using the trained second machine learning model, predict first sentiment scores for each of the first aspects.

The present invention belongs to the technical field of computer information processing, and provides an intelligent processing method and system for e-commerce reviews with multi-modal sentiment analysis. The method is as follows: real-time collect and preprocess e-commerce review data to obtain the preprocessed review data; respectively process through a trained text sentiment analysis model and a trained image sentiment analysis model to obtain the text sentiment score and the image sentiment score of each review; dynamically calculate the text weight and the image weight, and calculate the comprehensive sentiment score of each review; determine whether the current review is an abnormal review and perform an abnormal review mark; based on the comprehensive sentiment score and / or the abnormal review mark, automatically trigger an e-commerce review response operation with cross-system linkage. The present invention can automatically process a large amount of e-commerce reviews, accurately identify the user sentiment conveyed in the reviews through multi-modal sentiment analysis, and can automatically perform cross-system linkage to achieve a processing closed-loop of front-end real-time analysis and back-end business execution.

We provide the system. [Solution] A means for analyzing the search term entered by the user and breaking it down into its constituent elements, A means for collecting relevant evaluation information and attribute information based on the aforementioned components, A means for generating an emotion score based on the collected evaluation information and for identifying attributes based on the attribute information, A means for selecting the relevant information asset based on the aforementioned sentiment score and attribute, A means for presenting the selected information assets to the user, A means for generating a list based on user evaluation from the aforementioned presented information assets, A system that includes this.

The present application relates to the technical field of sentiment analysis, and particularly relates to a cross-domain aspect sentiment triple prototype constraint and relation correction method, comprising: performing context encoding, local semantic aggregation and fusion with domain identification on input text to obtain basic representation features of the text; constructing aspect phrase prototypes and opinion phrase prototypes based on target domain corpus, and performing phrase-level reorganization on source domain samples and screening cross-domain enhanced samples; generating candidate aspect spans and candidate opinion spans based on word boundary prediction results; performing relation correction and sentiment classification on candidate span pairs; and performing joint confidence constraint decoding by combining pairing scores and sentiment scores to output final aspect sentiment triples. The present application has the beneficial effect of introducing transfer priors under the condition of scarce target domain annotation data, reducing the interference of cross-domain expression differences on term matching, and improving the accuracy and stability of aspect term and opinion term pairing.

A computer-implemented conversation system includes a base large language model configured to receive a prompt from a user and provide a base response, an observer large language model configured to evaluate the base response and provide a feedback prompt to the base large language model to produce a refined response, a sentiment analysis module configured to calculate a sentiment score of the refined response, a coherence analysis module configured to calculate a coherence score of the refined response, a formality analysis module configured to calculate a formality score of the refined response, and a controller configured to present the refined response to the user when the sentiment score is above a sentiment threshold, when the coherence score is above a coherence threshold, and when a formality score is below a formality threshold. A conversation system and a grounded observer system are also disclosed.

The application provides a knowledge graph-based agricultural product online sales perception quality analysis method and system, which comprises the following steps: performing sentiment analysis on user comment texts to obtain original sentiment scores; constructing a knowledge graph of agricultural product online sales perception quality according to a historical comment text set; calibrating the original sentiment scores based on historical score deviation parameters associated with user entities in the knowledge graph to obtain individual perception scores; determining the confidence weight of each user comment according to the statistical distribution of all individual perception scores under the same commodity link; calculating the perception quality score of the agricultural product under the same commodity link according to the confidence weight and the individual perception score; and determining the perception quality trust score of the agricultural product under each brand based on the perception quality scores of the agricultural product under each commodity link and the corresponding brand data. The application can realize adaptive elimination of user scoring habits, thereby improving the accuracy of the evaluation of the perception quality of agricultural product online sales.

The invention relates to the technical field of text semantic analysis, and provides a complex-context-oriented text emotion multi-dimensional analysis method and system, and the method comprises the steps: accessing service interaction data, and carrying out the association segmentation of the service interaction data according to an association relationship, and obtaining a text unit sequence; performing context element analysis on each text unit to obtain context element information; extracting dominant emotion features and semantic deviation features, and calculating dominant emotion scores; determining an expected emotion score of the target text unit in combination with the context element information of the previous text unit; and generating a context correction coefficient based on the expected emotion score, the semantic deviation feature and the dominant emotion score, and correcting the dominant emotion score to obtain a corrected emotion result.