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In internet based life ‘Sentiment’ is a popular expression of the present patterns as there are heaps of work is being finished closing information or data mining. Utilizing sentiment examination different envision of events can be found, for example; human trafficking, burglary, ambiguous feelings and so on. Rapid miner, a social media mining stage which checks in the middle of sentiment relations through cloud mining techniques with a view to indicating the yield of a gross aftereffect of thought states. In this work, the authors have utilized rapid miner programming for investigating the continuous twitter cloud information mining methodology. Aftermath of this process, separation of human contemplation can be anticipated all through the varieties of their thinking sorts on each time based tweeting. The principle intention of this work is to demonstrate the performance execution between KNN and LR algorithms. Moreover, KNN performs superior to LR with 98% precision of genuine positivity upon individuals thinking examples or changes from constant data.

Social media mining can provide insights into a community’s perceptions which conventional approaches cannot observe. In this paper, we perform a sentiment analysis for measuring long-term trends in public opinion during the 2016 Indian demonetisation policy using Twitter data. We compare our findings to prior research and reports retrieved from media and sources. We utilise Rapid Miner sentiment classifier to a post-event of extending the deadline to deposit the forfeit banknotes. The results indicate an attitude that is predominantly continuing to oppose towards demonetisation policy implementation. We recommend from this study that a multi-lingual sentiment be employed to process non-polarised tweets in local languages in future work.

Social media is an ideal platform for influencers to share their experiences and product sentiments, as consumers frequently trust the recommendations of their peers. Consumer-created reviews and ratings are the preferred source of information about product and service value, price, and product quality. Social media mining is the process of storing, analyzing, and extracting useful patterns from social media data. Mining social information helps businesses in understanding the demand of their products like cars, movies, fashion goods, electronics, and so on. In this research, we present an analytical model which quantifies and engenders the insight of fused social information, gathered from multiple data sources. Subsequently, we discuss the results obtained by the proposed model for some movie use cases like “Angry Birds” by sourcing data from Twitter, Rotten Tomatoes, and the Internet Movie Database (IMDB).

The analysis of big data represents an important capability for companies and in research and teaching. Data scientists, confronted with complex system configuration and implementation tasks, require affordable and state-of-the-art solutions, which are flexibly configurable to enable diverse analytical research scenarios. In this research, we describe an architecture for the collection, preprocessing, and analysis of social media data based on Hadoop, which we used in a master-level course. We demonstrate how to configure and integrate different components of the Hadoop/Spark ecosystem in order to manage the collection of large data volumes as social media data streams over Web APIs, distributed data storage, the definition of schemas, data preprocessing, and feature extraction, as well as the calculation of descriptive statistics and predictive models. Three exemplary student projects, shortly described in this paper, demonstrate the versatility of the presented solution. Our results can serve as a blueprint for similar endeavors at other educational institutions.

In China, Sina-Weibo, with its rising popularity as a microblogging website, has inevitably attracted the attention of spammers. Spammers use myriad of techniques to evade security mechanisms and post spam messages, which are either unwelcome advertisements for the victim or lure victims in to clicking malicious URLs embedded in spam tweets. With the extensive application of machine learning in social media mining and Sina-Weibo’s development, we get many new ideas for the spammers detection. In this paper, we first make a comprehensive analysis specifically aiming at some new Sina-Weibo features rather than other social media, we further design a new feature set to detect spammers. We grab a large amount of Sina-Weibo data on the Internet and train the classifier with the algorithm GBDT. Through our experiments, we show that our new designed features are much more effective than some existing detector. And GBDT also has been significantly improved in both the accuracy and the FP-rate.