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- All Subjects: blockchain
- Creators: Manfredo, Mark
- Creators: Nakamura, Mutsumi
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.
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.
The purpose of this research is to better understand the potential use environment of a Dendritic Identifier within the current leafy green supply chain, including the exploration of potential costs of implementation as well as non-economic costs. This information was collected through an extensive review of literature and through the engagement in in-depth interviews with professionals that work in the growing, distribution, and processing of leafy greens. Food safety in the leafy green industry is growing in importance in the wake of costly outbreaks that resulted and recalls and lasting market damage. The Dendritic Identifier provides a unique identification tag that is unclonable, scannable, and compatible with blockchain systems. It is a digital trigger that can be implemented throughout the commercial leafy green supply chain to increase visibility from farm to fork for the consumer and a traceability system for government agencies to trace outbreaks. Efforts like the Food Safety Modernization Act, the Leafy Green Marketing Agreement, and other certifications aim at establishing science-based standards regarding soil testing, water, animal feces, imports, and more. The leafy green supply chains are fragmented in terms of tagging methods and data management services used. There are obstacles in implementing Dendritic Identifiers in that all parties must have systems capable of joining blockchain networks. While there is still a lot to take into consideration for implementation, solutions like the IBM Food Trust pose options for a more fluid transfer of information. Dendritic Identifiers beat out competing tagging technologies in that they work with cellphones, are low cost, and are blockchain compatible. Growers and processors are excited by the opportunity to showcase their extensive food safety measures. The next step in understanding the use environment is to focus on the retail distribution and the retailer specifically.
The purpose of this research is to better understand the potential use environment of a Dendritic Identifier within the current leafy green supply chain, including the exploration of potential costs of implementation as well as non-economic costs. This information was collected through an extensive review of literature and through the engagement in in-depth interviews with professionals that work in the growing, distribution, and processing of leafy greens. Food safety in the leafy green industry is growing in importance in the wake of costly outbreaks that resulted and recalls and lasting market damage. The Dendritic Identifier provides a unique identification tag that is unclonable, scannable, and compatible with blockchain systems. It is a digital trigger that can be implemented throughout the commercial leafy green supply chain to increase visibility from farm to fork for the consumer and a traceability system for government agencies to trace outbreaks. Efforts like the Food Safety Modernization Act, the Leafy Green Marketing Agreement, and other certifications aim at establishing science-based standards regarding soil testing, water, animal feces, imports, and more. The leafy green supply chains are fragmented in terms of tagging methods and data management services used. There are obstacles in implementing Dendritic Identifiers in that all parties must have systems capable of joining blockchain networks. While there is still a lot to take into consideration for implementation, solutions like the IBM Food Trust pose options for a more fluid transfer of information. Dendritic Identifiers beat out competing tagging technologies in that they work with cellphones, are low cost, and are blockchain compatible. Growers and processors are excited by the opportunity to showcase their extensive food safety measures. The next step in understanding the use environment is to focus on the retail distribution and the retailer specifically.