Item detection from a tweet is a common task to understand the current movies/topics attracting a large number of common users. However the unique characteristics of tweets (short and noisy content, and a large data volume) make the item detection a challenging task. Existing techniques proposed for item detection uses battery of one class classifier using key word matching techniques and SVM classifier and those techniques provide better accuracy but the features are extracted are found to be noisy, this is a major limitation in SVM classifier. In this system a SVM classifier with genetic algorithm optimization is proposed. In GA optimization we use 'accuracy 'of SVM as a fitness function; only the best features are selected. And this will improve accuracy for item detection and also the system provides user rating based on the polarity of tweets. This system is expected to improve in terms of classification accuracy when GA is combined with SVM.

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Item Detection Polarity Detection Twitter