The data mining is a technique by which the computational algorithms are trained for finding the similar patterns from the huge or raw set of data. The training of the algorithms is performed on the patterns which are required to extract from the data. The training of the algorithms can be supervised or unsupervised. The main advantage of the supervised learning algorithms, these are efficient, accurate and effective as compared to the unsupervised learning approaches. In this presented work the text classification is the key area of study. The text classification techniques are used to classify according to their categories or the domain specific knowledge. Thus the text classification has rich applications. Among a number of applications of the text classification the social media based text classification and the sentiment analysis of the user’s text is comparatively new work in the text mining. In this presented work the social media based text is mined for discovering the user sentiments or moods which are expressed using the twitter based text communication. Therefore big data analytics are used to performing the text classification. First the twitter data is hosted on the HDFS directory and then the features are computed using the Map-reduce technique. The collected features are then labelled using the NLP tool which is used to discover the part of speech composition of the text sentences. After parsing the text using NLP tool the Bayesian classifier is implemented for classification of the social media text. The implementation of the proposed technique is performed using the JAVA technology. After implementation the performance of the proposed system is evaluated in terms of accuracy and the complexity. Both the performance parameters show the proposed sentiment analysis technique is effective and accurate for classifying the social media text for orientation discovery of user text.

classification; sentiment analysis; supervised learning text orientation; text mining

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