The sentiment analysis methods and tools of AI technology in psychological counseling mainly include methods based on sentiment dictionary and methods based on machine learning.
The emotion dictionary-based approach relies on labeled affective dictionaries that contain words and their corresponding affective tendencies (positive, negative, or neutral). In the Chinese environment, the main emotional dictionaries are hownet and NTUSD compiled and published by National Taiwan University, and the "Synonym Lexicon" open source of the Information Retrieval Research Laboratory of Harbin Institute of Technology can also be used for the expansion of emotional dictionaries. This method determines the emotional tendencies of a text by matching words in the text with words in the emotion dictionary.
The machine learning-based method uses a large-scale corpus for training, and the algorithm automatically learns the association between text features and emotional tendencies. This method requires a large amount of annotated data to train the model, but once the model is trained, it can be inclined to new text.
In terms of tools, natural language processing (NLP) is a technology that processes text data to help understand and analyze the emotions that people express in social**, comments, or other forms. NLP tools use machine learning algorithms and large datasets to train models to automatically identify sentimental tendencies in text data.
In addition, there are some emotion recognition tools based on AI technology, such as sentiment analysis software, emotion recognition API, etc., which can automatically identify emotions in text, voice or images, and provide objective and quantitative emotion data support for psychological counseling.
In general, the sentiment analysis methods and tools of AI technology in psychological counseling provide more possibilities for psychological counselors to help them better understand the emotional state and needs of their clients.
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