Background:　Feelings　of　depression　can　be　caused　by　negative　life　events　(NLE)　such　as　the　death　of　a　family　member,　a　quarrel　with　one＇s　spouse,　job　loss,　or　strong　criticism　from　an　authority　figure.　The　automatic　and　accurate　identification　of　negative　life　event　language　patterns　(NLE-LP)　can　help　identify　individuals　potentially　in　need　of　psychiatric　services.　An　NLE-LP　combines　a　person　(subject)　and　a　reasonable　negative　life　event　(action),　e.g.　　or　＜　boyfriend:break-up＞.　Methods:　This　paper　proposes　an　analogical　reasoning　framework　which　combines　a　word　representation　approach　and　a　pattern　inference　method　to　mine/extract　NLE-LPs　from　psychiatric　consultation　documents.　Word　representation　approaches　such　as　skip-gram　(SG)　and　continuous　bag-of-words　(CBOW)　are　used　to　generate　word　embeddings.　Pattern　inference　methods　such　as　cosine　similarity　(COSINE)　and　cosine　multiplication　similarity　(COSMUL)　are　used　to　infer　patterns.　Results:　Experimental　results　show　our　proposed　analogical　reasoning　framework　outperforms　the　traditional　methods　such　as　positive　pairwise　mutual　information　(PPMI)　and　hyperspace　analog　to　language　(HAL),　and　can　effectively　mine　highly　precise　NLE-LPs　based　on　word　embeddings.　CBOW　with　COSINE　of　analogical　reasoning　is　the　best　word　representation　and　inference　engine.　In　addition,　both　word　embeddings　and　the　inference　engine　provided　by　the　analogical　reasoning　framework　can　further　be　used　to　improve　the　HAL　model.　Conclusions:　Our　proposed　framework　is　a　very　simple　matching　function　based　on　these　word　representation　approaches　and　is　applied　to　significantly　improve　HAL　model　mining　performance.　©　2019　The　Author(s).