A Study on the Multi-method Integrated Approach for Constructing Tibetan Emotional Dictionary
Currently,deep learning is gaining significant attention in the field of Tibetan sentiment analysis due to its superior performance compared to traditional machine learning methods,especially in the crucial role of sentiment word features.However,there are challenges in constructing a Tibetan sentiment dictionary,such as limited vocabulary,overreliance on machine translation systems,single dictionary matching sources,and lack of oral sentiment dictionaries.To address these issues,this article proposes a multi-method integrated approach for constructing a Tibetan sentiment dictionary.First,after conducting a statistical analysis of existing sentiment word annotation rules,a Tibetan sentiment word annotation rule is proposed as the main basis for sentiment word classification.Secondly,a method for constructing a multi-dictionary matching Tibetan sentiment dictionary is proposed and a Tibetan benchmark sentiment dictionary was constructed,to expand the vocabulary of the bench-mark dictionary,the SO-PMI and word2vec word vector similarity expansion methods were used.Furthermore,a Tibetan oral sentiment dictionary was created by manually screening the oral dictionaries of the three major Ti-betan dialects.Subsequently,the benchmark dictionary and the expanded dictionary were combined and dedupli-cated to obtain the Tibetan Written and Spoken Emotional Dictionary.Finally,an experiment was conducted to evaluate the performance of the Tibetan sentiment dictionary,demonstrating the feasibility of the proposed meth-od and the usability of the constructed dictionary.The accuracy,recall,and F-values of the experiment were 60.80％,90.31％,and 72.67％,respectively,indicating a good level of application and verifying the feasibility of the multi-method integrated approach to constructing the Tibetan sentiment dictionary.
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