Table of ContentsUsing Hadoop to Analyze Stock Market DataSentiment Analysis of Twitter Data Using HadoopProposed System Recently, YouTube began working with content providers (called YouTube Partners) to promote YouTube user viewing and sharing activities. The substantial benefit is to further increase its service and monetize more videos, which is crucial both for YouTube and its partners, as well as other relevant service providers. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”? Get an original essay In this article, our main contribution is to analyze the massive amounts of video data from the perspective of a YouTube partner. We're effectively using Insight, a new analytics service from YouTube that provides simple data analysis for partners. To provide actionable guidance from raw Insight data, we enable more complex investigations into the inherent features that affect video popularity. Our findings help YouTube partners rethink their current video publishing strategies, with more opportunities to attract more views. Significant studies have been done on user-generated data on YouTube. In addition to content shared by normal users, YouTube also introduced the Partner Program, through which premium content owners motivated by advertising revenue can upload high-quality copyrighted videos, serving a even wider user base. Examples of notable partners include industry giants such as EA, ESPN, and Warner Brothers. More and more small businesses and individuals have also partnered with YouTube to benefit from monetization of their videos, and their revenue has doubled for four years in a row. Machinima, one of the most popular YouTube partners, also received a significant investment from Google to produce more engaging videos, further implying the key role of YouTube partners. Using Hadoop to Analyze Stock Market Data Since stock markets generate a wide variety of unstructured data, this type of data can be analyzed using the Hadoop framework. A stock market data analysis project was carried out by taking a sample of data from the “New York Stock Exchange”. Using the Hadoop framework, the covariance of this stock data was calculated and aimed to solve both storage and processing issues related to a huge volume of data. The dataset used in this project was a CSV (comma separated) file which contains stock market information such as daily quotes, opening stock price, highest stock price, etc. on the New York Stock Exchange. Using Hive commands, a Hive table has been created. Once the table was created, the CSV data was loaded into the Hive table. Using Hive select queries, the covariance of the provided stock dataset for the entered year was calculated. From the covariance results, brokers provided key recommendations, including whether stock prices could move in an upward or reverse direction. Sentiment Analysis of Twitter Data Using Hadoop Sentiment analysis or opinion mining is defined as the categorization of opinions expressed on a social media platform about a given topic. subject. This project was undertaken to understand the reviewer's attitude towards a particular product or topic. Thanks to.