Stream data analysis using advanced machine learning approaches

Abstract

Stream data analysis involves the process of identifying the potential patterns from the data streams from various sources. Many of the data stream sources are social media applications. Social networks evolved as a mandate for sharing information instantly. Social networking applications are prominent among the internet user communities. Many social media websites are used for sharing the information instantly. newlineTwitter is one of the vibrant social networking websites for sharing small textual information within a short span of time. It is essential to identify the type of information shared on these websites. The twitter stream data analysis mainly involves the sentiment analysis process using various trained machine learning classifiers applied on a large collection of tweets. The classifiers are trained using a maximum number of polarity oriented words for effectively classifying the tweets. The trained classifiers at sentence level outperformed the keyword based classification method. The classified tweets are further analyzed for identifying the top tweets. The experimental results show that the sentiment analysis process predicted polarities of tweet and effectively identified the top tweets. In addition to the polarity prediction, score calculation of sentiment content on tweets is essential. newline newline