A parallel semantic sentiment analysis

The analysis of feelings(sentiment analysis or opinion mining) is the process of studying peoples opinion, emotion and how to consider an issue and make a decision into different categories like positive, negative and neutral in data mining. For example, sentiment analysis over Twitter provides organisations and individuals with a quick and effective way to monitor public feelings, such as knowing a customers opinion about a companys product. In this paper we propose a new method to analyze the feelings of the tweets in semantic and parallelizable way using the semantic resource Wordnet, By enriching the AFINN
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dictionary with semantics, the relation of semantics used is the synonymy. To solve the problem of the times execution of the analysis, if the dataset of the tweets is very large we decide to make the analysis in a parallel and distributed way using the Hadoop framework with the Hadoop Distributed File System HDFS and the programming model MapReduce. The aim of our work is to combine between several domains; the Information retrieval, semantic similarity, opinion mining or sentiment analysis and big data.