Posted: August 4th, 2022

Big data Analysis using Hadoop

Research Methodology, Data Analysis and Results, Ace my homework – Write my paper – Online assignment help tutors – Discussion

Big data Analysis using Hadoop

The predictive power of social media

Aims: Gain insight into the predictive power of social media by establishing correlations between the features of the data posted on online social media platforms and salient properties of a significant real-life event of your choice.

Background: You will develop tools to gain insight into salient features of a significant past or future real-life event (such as e.g., presidential or parliamentary elections, referenda, etc.) of your choice using a large dataset collected off an online social media site, such as Twitter. For the purposes of this project, you will have access to the 300G Twitter dataset comprising one month worth of Twitter data posted in 2014: 2024 – Essay Writing Service. Custom Essay Services Cheap, but you are also encouraged to propose and use an alternative dataset of your own provided it is sufficiently large, suitable for the studied problem, and approved by the supervisor). You may also consider collecting real-time data using the public APIs offered by some of the existing social media platforms, such as Twitter and Guardian.

You will use the tools you built to analyze various meta-data attributes associated with the data (such as time, location, hashtags, etc.) as well as properties of the content (e.g., sentiment score) to establish temporal, spatial, content-related, and other correlations between the real-life event you selected and the relevant social media properties. The report should reflect upon and draw general conclusions in regards to the predictive power of social media platforms in relation to the dynamics and outcomes of real-life events.

Your project must use one or a combination of the large-scale data engineering tools you have studied in CS5234. For example, past projects have used Hadoop/MapReduce to identify tweets relevant to the studied event, extract the attributes of relevance, and assign sentiment scores to the posted statuses using various Natural-Language Processing (NLP) techniques, such as [O’Connor, 2010 – Essay Writing Service: Write My Essay by Top-Notch Writer] and/or Stanford NLP libraries (http://nlp.stanford.edu/)

Data: You will have access to the 300G Twitter dataset on the department’s cloud or could use an alternative dataset of your own subject to the supervisor’s approval.

Tools(Hadoop/MapReduce): The project must use one or a combination of the large-scale data engineering tools studied in CS5234.

Early Deliverables

1. The precise specification of the project goals as agreed with the advisor along with data collection and analysis tools, and methodologies you plan to use to achieve the specified goals.

2. Intermediate report and programs showing and reflecting upon initial findings.

Final Deliverables

1. The software you built to achieve the project goals.

2. The final report will discuss techniques and findings and provide

From the Professor, the Draft of the dissertation should be ready on Wednesday, 09 August 2022

NB: 1. Chapters one and two will be provided via email

2. Data set will also be sent via email

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