Browsing by Author "Bartolome, Abigail"

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    Clustering and Topic Analysis in CS 5604 Information Retrieval Fall 2016
    Bartolome, Abigail; Islam, M. D.; Vundekode, Soumya (Virginia Tech, 2016-12-08)
    The IDEAL (Integrated Digital Event Archiving and Library) and Global Event and Trend Archive Research (GETAR) projects aim to build a robust Information Retrieval (IR) system by retrieving tweets and webpages from social media and the World Wide Web, and indexing them to be easily retrieved and analyzed. The project has been divided into different segments - Classification (CLA), Collection Management (tweets - CMT and webpages - CMW), Clustering and Topic Analysis (CTA), SOLR, and Front-End (FE). In building IR systems, documents are scored for relevance. To assist in determining a document’s relevance to a query, it is useful to know what topics are associated with the documents and what other documents relate to it. We, as the CTA team, used topic analysis and clustering techniques to aid in building this IR system. Our contributions were useful in scoring which documents are most relevant to a user’s query. We ran clustering and topic analysis algorithms on collections of tweets and webpages to identify the most discussed topics and grouped them into clusters along with their respective probabilities. We also labeled the topics and clusters, aiming for intuitive labels. The report and presentation cover the background, requirements, design and implementation of our contributions to this project. We evaluated the quality of our methodologies and describe improvements or future work that could be done to extend our project. Furthermore, we include a user manual and a developer manual to assist in any future work that may come from our efforts.
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    Sentiment and Topic Analysis
    Bartolome, 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.