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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 15 of 15 1 LEXICON BASED RULE EXTRACTION FOR SENTIMENT ANALYSIS UNDER BIG DATA ENVIRONMENT ... B. Sevugamoorthy. - : Zenodo, 2019 BASE Show details 2 LEXICON BASED RULE EXTRACTION FOR SENTIMENT ANALYSIS UNDER BIG DATA ENVIRONMENT ... B. Sevugamoorthy. - : Zenodo, 2019 BASE Show details 3 Hadoop vs. Spark: Impact on Performance of the Hammer Query Engine for Open Data Corpora Mauro Pelucchi; Giuseppe Psaila; Maurizio Toccu In: Algorithms ; Volume 11 ; Issue 12 (2018) BASE Show details 4 Summarization of Maryland Shooting Collection Kim, Yoonjin; Zhao, Shuqi; Fan, Yiyang. - : Virginia Tech, 2018 BASE Show details 5 Why Linguistic Fuzzy Rule Based Classification Systems perform well in Big Data Applications? Fernández Hilario, Alberto; Altalhi, Abdulrahman; Alshomrani, Saleh. - : Atlantis Press, 2017 BASE Show details 6 A Fine Grain Sentiment Analysis with Semantics in Tweets Navas-Delgado, Ismael; Aldana-Montes, Jose F.; Barba Gonzalez, Cristobal. - : International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 2016 BASE Show details 7 A MapReduce Approach to Address Big Data Classification Problems Based on the Fusion of Linguistic Fuzzy Rules Río García, Sara del; López, Victoria; Benítez Sánchez, José Manuel. - : ATLANTIS PRESS, 2015 BASE Show details 8 Exploring the Blacksburg Community Events Collection Antol, Stanislaw; Ayoub, Souleiman; Smith, Steve; Folgar, Carlos. - 2014 Abstract: This submission comes with a number of files: final_report.docx/final_report.pdf - The final report describing the approach that we took along with some discussion about what we learned and would want to do to improve the results. final_presentation.pptx/final_presentation.pdf - The final presentation that we gave to the class to explain our final approach. be_files.zip - The Blacksburg Events collection along with all additional files that were generated during our cleaning up and clustering. A more thorough explanation can be found in the appendix of the final report. be_code.zip - The Python code and Bash scripts that we used in our final approach. A more thorough explanation can be found in the appendix of the final report. be_results.zip - This file contains each of our cluster-of-interests output sentences as a file that has each sentence on a line. ; With the advent of new technology, especially the combination of smart phones and widespread Internet access, people are increasingly becoming absorbed in digital worlds worlds that are not bounded by geography. As such, some people worry about what this means for local communities. The Virtual Town Square project is an effort to harness people's use of these kinds of social networks, but with a focus on local communities. As part of the Fall 2014 CS4984 Computational Linguistics course, we explored a collection of documents, the Blacksburg Events Collection, that were mined from the Virtual Town Square for the town of Blacksburg, Virginia. We describe our activities to summarize this collection to inform newcomers about the local community. We begin by describing the approach that we took, which consisted of first cleaning our dataset and then applying the idea of Hierarchical Clustering to our collection. The core idea is to cluster the documents of our collection into sub-clusters, then cluster those sub-clusters, and then finally do sub-clustering on the sentences of the final sub-clusters. We then choose the sentences closest to the final sentence sub-cluster centroids as our summaries. Some of the summary sentences capture very relevant information about specific events in the community, but our final results still have a fair bit of noise and are not very concise. We then discuss some of the lessons that we learned throughout the course of the project, such as the importance of good project planning and quickly iterating on actual solutions instead of just discussing the multitude of approaches that can be taken. We then provide suggestions to improve upon our approach, especially ways to clean up the final sentence summaries. The appendix also contains a Developers Manual that describes the included files and the final code in detail. ; NSF DUE-1141209 and IIS-1319578 Keyword: blacksburg; cleaning up documents; clustering; community; computational linguistics; hadoop; hierarchical clustering; mahout; python; summarization; virtual town square URL: http://hdl.handle.net/10919/51135 BASE Hide details 9 Computational Linguistic Analysis of Earthquake Collections Wakeley, Christopher; Orleans-Pobee, Kwamina; Bialousz, Kenneth. - 2014 BASE Show details 10 Summarizing Fire Events with Natural Language Processing Plahn, Jordan; Trujillo, Michael; Lee, Hayden. - 2014 BASE Show details 11 Approaches to Automatically Constructing Polarity Lexicons for Sentiment Analysis on Social Networks Khuc, Vinh Ngoc In: http://rave.ohiolink.edu/etdc/view?acc_num=osu1343187623 (2012) BASE Show details 12 PARADISE Based Search Engine at TREC 2009 Web Track Shan, Dongdong; Zhao, Dongsheng; He, Jing... In: DTIC (2009) BASE Show details 13 Big Data Visualization : learn effective tools and techniques to separate big data into manageable and logical components for efficient data visualization Miller, James D. (VerfasserIn) IDS Mannheim Show details 14 A Text Sentimental Approach for Online Portals Using Hadoop Revathi S; Rajkumar N; Sathish S In: http://pnrsolution.org/Datacenter/Vol3/Issue1/133.pdf BASE Show details 15 Detecting Offensive Tweets via Topical Feature Discovery over a Large Scale Twitter Corpus Guang Xiang; Bin Fan; Ling Wang... In: http://www.cs.cmu.edu/%7Eguangx/papers/cikm12.pdf BASE Show details

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