Welcome to my Computer Science research paper archive. The research and papers were done as a team that I contributed towards. As a note, these papers are not published in a journal.

[1] Daniel Forgosh, Bemnut Nuru, Brandon Ubiera. 2019. Analyzing Hashtags Using Sentiment Analysis.
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Abstract: Tweets with specific hashtags from Twitter have been collected using a Twitter developer account. The hashtags that were collected are Facebook, Amazon, Apple, Netflix, and Google. Each hashtag will be its own data set. Stock data has been collected that correspond to each hashtag for the corresponding dates. Sentiment analysis was performed on all of the Twitter data sets to determine if each tweet is positive, negative, or neutral. A database was built to hold all of the tweets and stock data. A website user interface (UI) was built to view the data from the database and allow users to choose different criteria, and the website UI will dynamically generate graphs. These graphs can then be used to analyze the data to find interesting results about Twitter, Twitter users, as well as companies, companies’ stocks and items users tweet about.

[2] Daniel Forgosh, Bemnut Nuru, Brandon Ubiera. 2018. Airbnb Clustered Price Ranges.
Full Text | 3D Graph 1 | 3D Graph 2
Abstract: Clustering is a powerful method to data mine geographical data. It can group coordinate data points that consist of latitude and longitude. Aiming to use clustering as a basis, this paper data mines geographical data of all the Airbnbs in New York City to find the frequency distribution of the price within each cluster. The frequency distribution and clustering method used will find interesting data within the Airbnb data set. It is determined that K-means is to be used over other clustering strategies such as: density-based, hierarchy, and mobility. With K-means and choosing certain attributes within the data set, we were able to better understand the results by relating the data to geographical locations on the map of New York City.

[3] Daniel Forgosh, Ian Wittler. 2018. Stock Market Prediction of FAANG Stocks Using Twitter.
Full Text | Video Summary
Abstract: Clustering is a powerful method to data mine geographical data. It can group coordinate data points that consist of latitude and longitude. Aiming to use clustering as a basis, this paper data mines geographical data of all the Airbnbs in New York City to find the frequency distribution of the price within each cluster. The frequency distribution and clustering method used will find interesting data within the Airbnb data set. It is determined that K-means is to be used over other clustering strategies such as: density-based, hierarchy, and mobility. With K-means and choosing certain attributes within the data set, we were able to better understand the results by relating the data to geographical locations on the map of New York City.