CAJ | 학술논문

提出了一种中文微博情感词典构建方法。采用上下文熵的网络用语发现策略，通过 TF-IDF（term frequency-inverse document frequency）进行二次过滤得到网络用语；利用 SO-PMI（semantic orientation-pointwise mutual infor-mation）算法在已标注的微博语料库中计算网络用语的情感倾向值，构建网络用语情感词典；将词典应用到微博情感分类实验，并与朴素贝叶斯分类器的分类性能进行了比较分析。实验结果表明，直接利用微博情感词典的分类效果好于朴素贝叶斯分类器，并具有分类过程简单、快速等优势。
제출료일충중문미박정감사전구건방법。채용상하문적적망락용어발현책략，통과 TF-IDF（term frequency-inverse document frequency）진행이차과려득도망락용어；이용 SO-PMI（semantic orientation-pointwise mutual infor-mation）산법재이표주적미박어료고중계산망락용어적정감경향치，구건망락용어정감사전；장사전응용도미박정감분류실험，병여박소패협사분류기적분류성능진행료비교분석。실험결과표명，직접이용미박정감사전적분류효과호우박소패협사분류기，병구유분류과정간단、쾌속등우세。
A method of building Chinese microblog sentiment lexicon was proposed,which adopted the discovery strate-gies of context entropy for network language, acquired network languages from the secondary filtration by TF-IDF and computed the sentiment weights of network language by SO-PMI algorithm in the labeled corpus.The built lexicon was applied into the analysis experiments of micro-blog sentiment,which was compared with that of naive bayesian classifi-er.Experiment results showed that the efficacy of classification by the built micro-blog sentimental lexicon was better than that by naive bayesian classifier,and was simple and rapid in the classification process.