CS6604: Digital Libraries
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- Sentiment and Topic AnalysisBartolome, Abigail; Bock, Matthew; Vinayagam, Radha Krishnan; Krishnamurthy, Rahul (Virginia Tech, 2017-05-03)The IDEAL (Integrated Digital Event Archiving and Library) and Global Event and Trend Archive Research (GETAR) projects have collected over 1.5 billion tweets, and webpages from social media and the World Wide Web and indexed them to be easily retrieved and analyzed. This gives researchers an extensive library of documents that reflect the interests and sentiments of the public in reaction to an event. By applying topic analysis to collections of tweets, researchers can learn the topics of most interest or concern to the general public. Adding a layer of sentiment analysis to those topics will illustrate how the public felt in relation to the topics that were found. The Sentiment and Topic Analysis team has designed a system that joins topic analysis and sentiment analysis for researchers who are interested in learning more about public reaction to global events. The tool runs topic analysis on a collection of tweets, and the user can select a topic of interest and assess the sentiments with regard to that topic (i.e., positive vs. negative). This submission covers the background, requirements, design and implementation of our contributions to this project. Furthermore, we include data, scripts, source code, a user manual, and a developer manual to assist in any future work.
- Social Communities Knowledge Discovery: Approaches applied to clinical studyChandrasekar, Prashant (Virginia Tech, 2017-05)In recent efforts being conducted by the Social Interactome team, to validate hypotheses of the study, we have worked to make sense of the data that has been collected during two 16-week experiments and three Amazon Mechanical Turk deployments. The complexity in the data has made it challenging to discover insights/patterns. The goal of the semester was to explore newer methods to analyze the data. Through such discovery, we can test/validate hypotheses about the data, that would provide a direction for our contextual inquiry to predict attributes and behavior of participants in the study. The report and slides highlight two possible approaches that employ statistical relational learning for structure learning and network classification. Related files include data and software used during this study; results are given from the analyses undertaken.
- Tweet Analysis and Classification: Diabetes and Heartbleed Internet Virus as Use CasesKarajeh, Ola; Arachie, Chidubem; Powell, Edward; Hussein, Eslam (Virginia Tech, 2019-12-24)The proliferation of data on social media has driven the need for researchers to develop algorithms to filter and process this data into meaningful information. In this project, we consider the task of classifying tweets relative to some topic or event and labeling them as informational or non-informational, using the features in the tweets. We focus on two collections from different domains: a diabetes dataset in the health domain and a heartbleed dataset in the security domain. We show the performance of our method in classifying tweets in the different collections. We employ two approaches to generate features for our models: 1) a graph based feature representation and 2) a vector space model, e.g., with TF-IDF weighting or a word embedding. The representations generated are fed into different machine learning algorithms (Logistic Regression, Naïve Bayes, and Decision Tree) to perform the classification task. We evaluate these approaches using metrics (accuracy, precision, recall, and F1-score) on a held out test dataset. Our results show that we can generalize our approach with tweets across different domains.