IndexUsing Hadoop to Analyze Stock Market Data Sentiment Analysis of Twitter Data Using Hadoop Proposed System Recently, YouTube has started collaborating with content providers (known as YouTube Partners) to promote users' viewing and sharing activities. The substantial benefit is to further scale up its service and monetize more videos, which is crucial for both 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 Shouldn't Be Banned"? Get an original essay In this article, our main contribution is to analyze the huge amounts of video data from the perspective of a YouTube partner. We effectively use Insight, a new YouTube analytics service that offers simple data analysis for partners. To provide practical guidance from Insight's raw data, we enable more complex investigations into the inherent characteristics that impact video popularity. Our findings help YouTube partners redesign their current video publishing strategies, giving them more opportunities to attract more views. There have been significant studies conducted on user-generated data on YouTube. In addition to content shared by regular users, YouTube also introduced the Partner Program, through which premium content owners, motivated by advertising revenue, can upload high-quality copyrighted videos, serving an even larger user base . Examples of notable partners include industry giants such as EA, ESPN, and Warner Brothers. More and more small businesses and individuals have partnered with YouTube to take advantage of monetizing their videos, and their revenue has doubled for four consecutive years. Machinima, one of the most popular YouTube partners, has also received significant investment from Google to produce more engaging videos, further implicating 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 conducted by taking a sample data set of the “New York Stock Exchange”. Using the Hadoop Framework, covariance was calculated for this stock data with the goal of solving both storage and processing issues related to a huge volume of data. The dataset used in this project was a comma separated file (CSV) which contains stock information such as daily quotes, stock opening price, stock high price, etc. on the New York Stock Exchange. Using Hive commands, a Hive table was created. Once the table was created, the CSV data was loaded into the Hive table. Using Hive selection queries, we calculated the covariance for the provided stock dataset for the year entered. From the covariance results, stockbrokers provided key recommendations, including whether stock prices will move up or in the opposite 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 subject. This project was undertaken to understand the comment writer's attitude towards a particular product or topic. Using sentiment analysis, you can determine whether people's overall attitude is positive, or negative.