心理咨询的LIWC语言特征对咨询效果的预测

The Association between Language Use of LIWC and Treatment Outcome in Psychotherapy

语言是心理咨询的重要内容。语言探索和词频统计(language inquiry and word count,LIWC)被广泛用于分析语言使用的心理意义。本研究收集了28名当事人共144次有效咨询录音及每次会谈后当事人的症状自评结果。将录音转录成文本后,抽取当事人的LIWC语言特征,进行主成分降维,结果获得8个因子,解释了总体方差的75.11%。一般线性回归中,躯体感受、功能词、情绪、认知和口语赘词对当事人的症状水平具有显著的预测作用,进一步多层线性模型中,躯体感受和情绪用词显著预测咨询效果。LIWC文本特征能够呈现与当事人症状相关的信息,为未来计算机自动化监控咨询过程和结果提供了新视角。

Language in psychotherapy provides important information about the process and outcome of counseling.Qualitative coding is a traditional and commonly used method for process and outcome research.However,it costs much time and money,and it is hard to deal with a large scale of psychotherapy data.Considering the limitations of qualitative coding,Language Inquiry and Word Count(LIWC)has become an alternative and convenient method to explore the relationship between language use and psychological status.Evidence showed that LIWC words revealed people’s mental health and thinking styles.The present study analyzed the association between clients’language use during psychotherapy and their symptoms in a Chinese context,to identify the possibility of predicting clients’improvement by analyzing natural language.Methods:The recordings of psychotherapy and the outcome questionnaires of 28 clients were collected.Most of participants were female because data was collected in the mental health center of a normal university where male to female ratio was 1:8.Each client received 5.5 sessions of counselling averagely.13 therapists took part in the study and all of them were trained.10 recordings were not included in the analysis due to loss of data.The remaining 144 recordings were transcribed into texts by research assistants word by word.After checking mistakes,data cleaning was conducted including cutting words and cutting off talk turns.Then,the LIWC words frequencies were calculated by scripts written in python 3.6.According to previous studies,raw word frequencies were transformed into Z score.The principal component analysis was conducted to find out the main factors of LIWC features associated with clients’symptoms.Moreover,the main factors of LIWC features were used to predict the clients’improvement across sessions in a multilevel model.Results:results showed that 31 categories of LIWC features were significantly associated with clients’symptom level.Results of principal component analysis revealed that 8 factors interpreted 75.11%of the variance,including biological feelings words,social relationship words,time words,cognition words,function words,emotion words,superfluous words and filler words.Among the factors,frequencies of biological feelings words,function words,emotion words,cognition words and filler words could predict the symptom level of clients in a general linear regression model,to determine the associations between the change of LIWC words across sessions and the change of clients’symptom level.In a multilevel model,it was promising to find that biological feelings and emotion words could be used to predict the outcome of psychotherapy controlling the time variable.Conclusions:as a preliminary research that applied text analysis in psychotherapy,the present study revealed that the words in psychotherapy had potential to predict clients symptoms.Although there were some limitations such as limited participants,rough LIWC features and simple algorithm,the present study gives us some valuable implications.With the development of artificial intelligence,more sophisticated text analysis tools,even audio analysis could be applied in automatically monitoring the process and outcome of psychotherapy which would help clients and therapists a lot.Future work can focus on a more accurate monitoring model of psychotherapy by combining more useful features and algorithms.