In the ROCLING 2025 dimensional sentiment analysis task, we present EmoTracer. It is an emotion-space-based system for analyzing doctors’ self-reflection texts. The system uses XLNet, BERT, and LSTM models. It is trained on the SLAKE medical dataset and Chinese datasets, such as Chinese EmoBank and NRC-VAD. This helps the system capture the possible emotional changes of doctors when they write patient-related reflections. EmoTracer converts texts into Valence and Arousal scores. The experiments show about 60% accuracy, a Pearson correlation coefficient (PCC) of 0.9, and a mean absolute error (MAE) of 0.3. These results can help support mental health management. The system also has a simple front-end UI. Users can enter texts and see the analysis results. This demonstrates the full functionality of the EmoTracer system.

Currently, most sentiment analysis techniques are primarily applied to general texts such as social media or news reports, and there is still a relative gap in emotion recognition within the medical field. Self reflection involves communication between individuals and their inner selves, which has a positive impact on people’s future lives. This article aims to design a classification model for reflective texts aimed at medical professionals to fill gaps in sentiment analysis within the medical field. This task used a BERT model, trained on a dataset from the Chinese EmoBank, and evaluated using the test set provided by the ROCLING 2025 Dimensional Sentiment Analysis – Shared Task. The assessment results show that Valence and Arousal’s PCC scores are 0.76 and 0.58 respectively, while the MAE scores are 0.53 and 0.82, respectively.

This paper presents two vector representations proposed by National Chiayi University (NCYU) about phrased-based sentiment detection which was used to compete in dimensional sentiment analysis for Chinese phrases (DSACP) at IJCNLP 2017. The vector-based sentiment phraselike unit analysis models are proposed in this article. E-HowNet-based clustering is used to obtain the values of valence and arousal for sentiment words first. An out-of-vocabulary function is also defined in this article to measure the dimensional emotion values for unknown words. For predicting the corresponding values of sentiment phrase-like unit, a vectorbased approach is proposed here. According to the experimental results, we can find the proposed approach is efficacious.

This paper presents a Chinese spelling check approach based on language models combined with string match algorithm to treat the problems resulted from the influence caused by Cantonese mother tone. N-grams first used to detecting the probability of sentence constructed by the writers, a string matching algorithm called Knuth-Morris-Pratt (KMP) Algorithm is used to detect and correct the error. According to the experimental results, the proposed approach can detect the error and provide the corresponding correction.

Mandarin is not simple language for foreigner. Even using Mandarin as the mother tongue, they have to spend more time to learn when they were child. The following issues are the reason why causes learning problem. First, the word is envolved by Hieroglyphic. So a character can express meanings independently, but become a word has another semantic. Second, the Mandarin’s grammars have flexible rule and special usage. Therefore, the common grammatical errors can classify to missing, redundant, selection and disorder. In this paper, we proposed the structure of the Recurrent Neural Networks using Long Short-term memory (RNN-LSTM). It can detect the error type from the foreign learner writing. The features based on the word vector and part-of-speech vector. In the test data found that our method in the detection level of recall better than the others, even as high as 0.9755. That is because we give the possibility of greater choice in detecting errors.