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A public dataset, with a variety of properties suitable for sentimentanalysis [1], event prediction, trend detection and other text miningapplications, is needed in order to be able to successfully perform analysisstudies. The vast majority of data on social media is text-based and it is notpossible to directly apply machine learning processes into these raw data,since several different processes are required to prepare the data before theimplementation of the algorithms. For example, different misspellings of sameword enlarge the word vector space unnecessarily, thereby it leads to reducethe success of the algorithm and increase the computational power requirement.This paper presents an improved Turkish dataset with an effective spellingcorrection algorithm based on Hadoop [2]. The collected data is recorded on theHadoop Distributed File System and the text based data is processed byMapReduce programming model. This method is suitable for the storage andprocessing of large sized text based social media data. In this study, moviereviews have been automatically recorded with Apache ManifoldCF (MCF) [3] anddata clusters have been created. Various methods compared such as Levenshteinand Fuzzy String Matching have been proposed to create a public dataset fromcollected data. Experimental results show that the proposed algorithm, whichcan be used as an open source dataset in sentiment analysis studies, have beenperformed successfully to the detection and correction of spelling errors.

Preparation of Improved Turkish DataSet for Sentiment Analysis in Social Media