In　recent　years,　online　sentiment　texts　are　generated　by　users　in　various　domains　and　in　different　languages.　Binary　polarity　classification　(positive　or　negative)　on　business　sentiment　texts　can　help　both　companies　and　customers　to　evaluate　products　or　services.　Sometimes,　the　polarity　of　sentiment　texts　can　be　modified,　making　the　polarity　classification　difficult.　In　sentiment　analysis,　such　modification　of　polarity　is　termed　as　polarity　shifting,　which　shifts　the　polarity　of　a　sentiment　clue　(emotion,　evaluation,　etc.).　It　is　well　known　that　detection　of　polarity　shifting　can　help　improve　sentiment　analysis　in　texts.　However,　to　detect　polarity　shifting　in　corpora　is　challenging:　(1)　polarity　shifting　is　normally　sparse　in　texts.　making　human　annotation　difficult;　(2)　corpora　with　dense　polarity　shifting　are　few;　we　may　need　polarity　shifting　patterns　from　various　corpora.　In　this　article,　an　approach　is　presented　to　extract　polarity　shifting　patterns　from　any　text　corpus.　For　the　first　time,　we　proposed　to　select　texts　rich　in　polarity　shifting　by　the　idea　of　natural　annotation,　which　is　used　to　replace　human　annotation.　With　a　sequence　mining　algorithm,　the　selected　texts　are　used　to　generate　polarity　shifting　pattern　candidates,　and　then　we　rank　them　by　C-value　before　human　annotation.　The　approach　is　tested　on　different　corpora　and　different　languages.　The　results　show　that　our　approach　can　capture　various　types　of　polarity　shifting　patterns,　and　some　patterns　are　unique　to　specific　corpora.　Therefore,　for　better　performance,　it　is　reasonable　to　construct　polarity　shifting　patterns　directly　from　the　given　corpus.