However, complete deep learning is time-consuming, and text representation is still dominated by word vectors, leading to difficulty in capturing implicit semantic features[PERSON] The BERT pre-trained model proposed by Google [25], with a multi-layer bi-directional Transformer [26] as the main body, extracts contextual semantic features and highlights sentence focus using two pre-trained methods, the Masked Language Model (MLM) and Next Sentence Prediction (NSP)[PERSON] Downstream fine-tuning based on the BERT pre-trained model can be applied to new tasks and save a lot of retraining time. However, BERT's strategy of masking, with Chinese character granularity, obviously destroys Chinese entities and does not conform to the characteristics of Chinese text. In contrast, the Ernie model proposed by Baidu [27] learns syntactic and semantic information from these units implicitly through masking units such as phrases and entities. The Ernie model outperforms models such as BERT on various Chinese Natural Language Processing tasks. The lightweight Ernie model Ernie-Tiny, proposed immediately after, is compressed from 12 layers to 3 layers, which significantly reduces model parameters, improves training speed, and provides good text analysis, making it more suitable for practical applications and development[PERSON] In recent years, some researchers have been devoted to using Ernie's pre-trained model together with Bi-directional Gated Recurrent Unit (BiGRU) to perform Chinese text analysis. [PERSON] et al. [28] used Ernie and BiGRU for Chinese text classification tasks[PERSON] [PERSON] et al. [29] proposed an improved Chinese short text classification method based on the Ernie_BiGRU model. In terms of various evaluation metrics for the text classification task, the joint use of Ernie and BiGRU performed well. However, traditional Chinese text analysis methods are limited by the accuracy of word separation and have difficulties in dealing with factors such as context, syntax, and word co-occurrence relationships, which makes Chinese text sentiment analysis less effective[PERSON] Moreover, the pre-trained model has huge parameters and consumes many computational resources. Therefore, this study intends to propose a new subjective Chinese text sentiment analysis model to address the problems of insufficient semantic feature extraction and low model efficiency. Since text sentiment analysis technology is less applied in the field of mental health care, this study developed an interactive application prototype that visualizes text sentiment analysis results based on this model. The application prototype can help people understand their emotional state in daily life in a timely and intuitive manner, which is important for people's daily mental health. The main contributions of this paper include:  
1. A subjective text sentiment analysis model ET_s_BG+p based on Ernie-Tiny and BiGRU is proposed, which can achieve efficient semantic feature representation and sentiment analysis for Chinese subjective texts with complex semantics, diverse sentence forms, and short texts. 2. Based on the ET_s_BG+p model, an interactive application prototype integrating text input, sentiment analysis, intelligent interaction, and other functions is developed to provide a reference for sentiment visualization and interactive application design.