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Social Media Sentiment as a Comparative Business Metric - Using Logical Appeals Among Businesses to Understand Consumer Reaction and Engagement with Various Brands

Description

With the discovery of “Big Data” and the positive impacts properly using data can have on any and every business, it is no wonder that there has been an explosion of companies choosing to implement many possible uses of data.

With the discovery of “Big Data” and the positive impacts properly using data can have on any and every business, it is no wonder that there has been an explosion of companies choosing to implement many possible uses of data. Consumers and any people who may not fully understand the process of collecting, analyzing, and visualizing data may be more easily swayed towards believing something that might not necessarily be true or represented accurately. Often it may feel like every hot topic issue has groups on both sides of the issues using seemingly objective data to prove why their side is correct. Seeing two contradictory sides with seemingly factual data can leave many people confused and unsure what the correct course of action is. With this in mind, I realized that there was a chance the businesses could be creating similar misrepresentations of data to sway customers that the company’s product or service is absolutely a necessity in their lives. After all, the world of marketing and understanding consumer preference is a wildly changing and constant moving target that companies have to navigate. Using data surrounding their products and services to create a desire in consumers to buy and use their offerings seems like a surefire way to successfully target market segments.
As I researched and conducted initial analysis for this project, I quickly ran into a few roadblocks that lead to me needing to pivot off of certain ideas and adapt my initial plans to fit what was actually being done in the current marketing environment. In reality, most businesses are not up for taking the risk of explicitly giving real metrics of their products and services to customers. Due to this, my thesis evolved into finding other ways that companies would use logical appeals to represent their products and comparatively analyze how these companies choose to represent themselves on a social media platform.

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Agent

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Date Created
2020-05

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Twitter Sentiment Analysis For Bitcoin Price Prediction

Description

Cryptocurrencies are notorious for its volatility. But with its incredible rise in price, Bitcoin keep being on the top among the trending topics on social media. Although doubts continue to rise with price, Bloomberg even make critics on Bitcoin as

Cryptocurrencies are notorious for its volatility. But with its incredible rise in price, Bitcoin keep being on the top among the trending topics on social media. Although doubts continue to rise with price, Bloomberg even make critics on Bitcoin as ‘the biggest bubble in the history’, some investors still hold strong enthusiasm and confidence towards Bitcoin. As contradicting opinions increase, it is worthy to dive into discussions on social media and use a scientific method to evaluate public’s non-negligible role in crypto price fluctuation.

Sentiment analysis, which is a notably method in text mining, can be used to extract the sentiment from people’s opinion. It then provides us with valuable perception on a topic from the public’s attitude, which create more opportunities for deeper analysis and prediction.

The thesis aims to investigate public’s sentiment towards Bitcoin through analyzing 10 million Bitcoin related tweets and assigning sentiment points on tweets, then using sentiment fluctuation as a factor to predict future crypto fluctuation. Price prediction is achieved by using a machine learning model called Recurrent Neural Network which automatically learns the pattern and generate following results with memory. The analysis revels slight connection between sentiment and crypto currency and the Neural Network model showed a strong connection between sentiment score and future price prediction.

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Agent

Created

Date Created
2018-12

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The Value of Emotion: Exploring the Use and Impact of Sentiment Analysis in Various Industries

Description

This thesis studies the area of sentiment analysis and its general uses, benefits, and limitations. Social networking, blogging, and online forums have turned the Web into a vast repository of comments on many topics. Sentiment analysis is the process of

This thesis studies the area of sentiment analysis and its general uses, benefits, and limitations. Social networking, blogging, and online forums have turned the Web into a vast repository of comments on many topics. Sentiment analysis is the process of using software to analyze social media to gauge the attitudes or sentiments of the users/authors concerning a particular subject. Sentiment analysis works by processing (data mining) unstructured textual evidence using natural language processing and machine learning to determine a positive, negative, or neutral measurement. When utilized correctly, sentiment analysis has the potential to glean valuable insights into consumers' minds, which in turn leads to increased revenue and improved customer satisfaction for businesses. This paper looks at four industries in which sentiment analysis is being used or being considered: retail/services, politics, healthcare, and finances. The goal of the thesis will be to explore whether sentiment analysis has been used successfully for economic or social benefit and whether it is a practical solution for analyzing consumer opinion.

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Agent

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Date Created
2014-05

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Timing, Diligence, and Innovation: A Case-Study Determining Mobile Application Success

Description

The purpose of this thesis was to develop a tool to provide information and data for design teams to use throughout the mobile application design process. Ideally, this would enable teams to see patterns in iterative design, and ultimately use

The purpose of this thesis was to develop a tool to provide information and data for design teams to use throughout the mobile application design process. Ideally, this would enable teams to see patterns in iterative design, and ultimately use data-driven analysis to make their own decisions. The initial problem was a lack of available information offered by mobile application design teams—the initial goal being to work closely with design teams to learn their decision-making methodology. However, every team that was reached out to responded with rejection, presenting a new problem: a lack of access to quality information regarding the decision-making process for mobile applications. This problem was addressed by the development of an ethical hacking script that retrieves reviews in bulk from the Google Play Store using Python. The project was a success—by feeding an application’s unique Play Store ID, the script retrieves a user-specified amount of reviews (up to millions) for that mobile application and the 4 “recommended” applications from the Play Store. Ultimately, this thesis proved that protected reviews on the Play Store can be ethically retrieved and used for data-driven decision making and identifying patterns in an application’s iterative design. This script provides an automated tool for teams to “put a finger on the pulse” of their target applications.

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Agent

Created

Date Created
2016-12