Matching Items (13)

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Sentiment Analysis of Public Perception Towards Transgender Rights on Twitter

Description

The fight for equal transgender rights is gaining traction in the public eye, but still has a lot of progress to make in the social and legal spheres. Since public

The fight for equal transgender rights is gaining traction in the public eye, but still has a lot of progress to make in the social and legal spheres. Since public opinion is critical in any civil rights movement, this study attempts to identify the most effective methods to elicit public reactions in support of transgender rights. Topic analysis through Latent Dirichlet Allocation is performed on Twitter data, along with polarity sentiment analysis, to track the subjects which gain the most effective reactions over time. Graphing techniques are used in an attempt to visually display the trends in topics. The topic analysis techniques are effective in identifying the positive and negative trends in the data, but the graphing algorithm lacks the ability to comprehensibly display complex data with more dimensionality.

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Created

Date Created
  • 2016-12

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Machine Learning: A Sentiment Analysis of Customer Reviews

Description

Machine learning is the process of training a computer with algorithms to learn from data and make informed predictions. In a world where large amounts of data are constantly collected,

Machine learning is the process of training a computer with algorithms to learn from data and make informed predictions. In a world where large amounts of data are constantly collected, machine learning is an important tool to analyze this data to find patterns and learn useful information from it. Machine learning applications expand to numerous fields; however, I chose to focus on machine learning with a business perspective for this thesis, specifically e-commerce.

The e-commerce market utilizes information to target customers and drive business. More and more online services have become available, allowing consumers to make purchases and interact with an online system. For example, Amazon is one of the largest Internet-based retail companies. As people shop through this website, Amazon gathers huge amounts of data on its customers from personal information to shopping history to viewing history. After purchasing a product, the customer may leave reviews and give a rating based on their experience. Performing analytics on all of this data can provide insights into making more informed business and marketing decisions that can lead to business growth and also improve the customer experience.
For this thesis, I have trained binary classification models on a publicly available product review dataset from Amazon to predict whether a review has a positive or negative sentiment. The sentiment analysis process includes analyzing and encoding the human language, then extracting the sentiment from the resulting values. In the business world, sentiment analysis provides value by revealing insights into customer opinions and their behaviors. In this thesis, I will explain how to perform a sentiment analysis and analyze several different machine learning models. The algorithms for which I compared the results are KNN, Logistic Regression, Decision Trees, Random Forest, Naïve Bayes, Linear Support Vector Machines, and Support Vector Machines with an RBF kernel.

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Created

Date Created
  • 2020-05

Predicting Bitcoin Price Trend using Sentiment Analysis

Description

In this paper I defend the argument that public reaction to news headlines correlates with the short-term price direction of Bitcoin. I collected a month's worth of Bitcoin data consisting

In this paper I defend the argument that public reaction to news headlines correlates with the short-term price direction of Bitcoin. I collected a month's worth of Bitcoin data consisting of news headlines, tweets, and the price of the cryptocurrency. I fed this data into a Long Short-Term Memory Neural Network and built a model that predicted Bitcoin price for a new timeframe. The model correctly predicted 75% of test set price trends on 3.25 hour time intervals. This is higher than the 53.57% accuracy tested with a Bitcoin price model without sentiment data. I concluded public reaction to Bitcoin news headlines has an effect on the short-term price direction of the cryptocurrency. Investors can use my model to help them in their decision-making process when making short-term Bitcoin investment decisions.

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Created

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

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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Created

Date Created
  • 2018-12

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Feature Extraction on Sentiment Attitude Values to Better Predict the Stock Market Using Twitter Sentiment

Description

Behavioral economics suggests that emotions can affect an individual’s decision making. Recent research on this idea’s application on large societies hints that there may exist some correlation or maybe even

Behavioral economics suggests that emotions can affect an individual’s decision making. Recent research on this idea’s application on large societies hints that there may exist some correlation or maybe even some causation relationship between public sentiment—at least what can be pulled from Twitter—and the movement of the stock market. One major result of consistent research on whether or not public sentiment can predict the movement of the stock market is that public sentiment, as a feature, is becoming more and more valid as a variable for stock-market-based machine learning models. While raw values typically serve as invaluable points of data, when training a model, many choose to “engineer” new features for their models—deriving rates of change or range values to improve model accuracy.
Since it doesn’t hurt to attempt to utilize feature extracted values to improve a model (if things don’t work out, one can always use their original features), the question may arise: how could the results of feature extraction on values such as sentiment affect a model’s ability to predict the movement of the stock market? This paper attempts to shine some light on to what the answer could be by deriving TextBlob sentiment values from Twitter data, and using Granger Causality Tests and logistic and linear regression to test if there exist a correlation or causation between the stock market and features extracted from public sentiment.

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Created

Date Created
  • 2020-05

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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

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.

Contributors

Agent

Created

Date Created
  • 2016-12

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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

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

Created

Date Created
  • 2014-05

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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

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.

Contributors

Agent

Created

Date Created
  • 2020-05

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Mining signed social networks using unsupervised learning algorithms

Description

Due to vast resources brought by social media services, social data mining has

received increasing attention in recent years. The availability of sheer amounts of

user-generated data presents data scientists both opportunities

Due to vast resources brought by social media services, social data mining has

received increasing attention in recent years. The availability of sheer amounts of

user-generated data presents data scientists both opportunities and challenges. Opportunities are presented with additional data sources. The abundant link information

in social networks could provide another rich source in deriving implicit information

for social data mining. However, the vast majority of existing studies overwhelmingly

focus on positive links between users while negative links are also prevailing in real-

world social networks such as distrust relations in Epinions and foe links in Slashdot.

Though recent studies show that negative links have some added value over positive

links, it is dicult to directly employ them because of its distinct characteristics from

positive interactions. Another challenge is that label information is rather limited

in social media as the labeling process requires human attention and may be very

expensive. Hence, alternative criteria are needed to guide the learning process for

many tasks such as feature selection and sentiment analysis.

To address above-mentioned issues, I study two novel problems for signed social

networks mining, (1) unsupervised feature selection in signed social networks; and

(2) unsupervised sentiment analysis with signed social networks. To tackle the first problem, I propose a novel unsupervised feature selection framework SignedFS. In

particular, I model positive and negative links simultaneously for user preference

learning, and then embed the user preference learning into feature selection. To study the second problem, I incorporate explicit sentiment signals in textual terms and

implicit sentiment signals from signed social networks into a coherent model Signed-

Senti. Empirical experiments on real-world datasets corroborate the effectiveness of

these two frameworks on the tasks of feature selection and sentiment analysis.

Contributors

Agent

Created

Date Created
  • 2017

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Event analytics on social media: challenges and solutions

Description

Social media platforms such as Twitter, Facebook, and blogs have emerged as valuable

- in fact, the de facto - virtual town halls for people to discover, report, share and

communicate with

Social media platforms such as Twitter, Facebook, and blogs have emerged as valuable

- in fact, the de facto - virtual town halls for people to discover, report, share and

communicate with others about various types of events. These events range from

widely-known events such as the U.S Presidential debate to smaller scale, local events

such as a local Halloween block party. During these events, we often witness a large

amount of commentary contributed by crowds on social media. This burst of social

media responses surges with the "second-screen" behavior and greatly enriches the

user experience when interacting with the event and people's awareness of an event.

Monitoring and analyzing this rich and continuous flow of user-generated content can

yield unprecedentedly valuable information about the event, since these responses

usually offer far more rich and powerful views about the event that mainstream news

simply could not achieve. Despite these benefits, social media also tends to be noisy,

chaotic, and overwhelming, posing challenges to users in seeking and distilling high

quality content from that noise.

In this dissertation, I explore ways to leverage social media as a source of information and analyze events based on their social media responses collectively. I develop, implement and evaluate EventRadar, an event analysis toolbox which is able to identify, enrich, and characterize events using the massive amounts of social media responses. EventRadar contains three automated, scalable tools to handle three core event analysis tasks: Event Characterization, Event Recognition, and Event Enrichment. More specifically, I develop ET-LDA, a Bayesian model and SocSent, a matrix factorization framework for handling the Event Characterization task, i.e., modeling characterizing an event in terms of its topics and its audience's response behavior (via ET-LDA), and the sentiments regarding its topics (via SocSent). I also develop DeMa, an unsupervised event detection algorithm for handling the Event Recognition task, i.e., detecting trending events from a stream of noisy social media posts. Last, I develop CrowdX, a spatial crowdsourcing system for handling the Event Enrichment task, i.e., gathering additional first hand information (e.g., photos) from the field to enrich the given event's context.

Enabled by EventRadar, it is more feasible to uncover patterns that have not been

explored previously and re-validating existing social theories with new evidence. As a

result, I am able to gain deep insights into how people respond to the event that they

are engaged in. The results reveal several key insights into people's various responding

behavior over the event's timeline such the topical context of people's tweets does not

always correlate with the timeline of the event. In addition, I also explore the factors

that affect a person's engagement with real-world events on Twitter and find that

people engage in an event because they are interested in the topics pertaining to

that event; and while engaging, their engagement is largely affected by their friends'

behavior.

Contributors

Agent

Created

Date Created
  • 2014