The sequential pattern mining was widely used to solve various business problems, including frequent user click pattern, customer analysis of buying product, gene microarray data analysis, etc. Many studies were going on these pattern mining to extract insightful data. All the studies were mostly concentrated on high utility sequential pattern mining (HUSP) with positive values without a distributed approach. All the existing solutions are centralized which incurs greater computation and communication costs. In this paper, we introduce a novel algorithm for mining HUSPs including negative item values in support of a distributed approach. We use the Hadoop map reduce algorithms for processing the data in parallel. Various pruning techniques have been proposed to minimize the search space in a distributed environment, thus reducing the expense of processing. To our understanding, no algorithm was proposed to mine High Utility Sequential Patterns with negative item values in a distributed environment. So, we design a novel algorithm called DHUSP-N (Distributed High Utility Sequential Pattern mining with Negative values). DHUSP-N can mine high utility sequential patterns considering the negative item utilities from Bigdata.