Sentiment and Topic Analysis
| dc.contributor.author | Bartolome, Abigail | en |
| dc.contributor.author | Bock, Matthew | en |
| dc.contributor.author | Vinayagam, Radha Krishnan | en |
| dc.contributor.author | Krishnamurthy, Rahul | en |
| dc.date.accessioned | 2017-06-02T14:21:51Z | en |
| dc.date.available | 2017-06-02T14:21:51Z | en |
| dc.date.issued | 2017-05-03 | en |
| dc.description.abstract | 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. | en |
| dc.description.notes | Sentiment_and_Topic_Analysis.pdf: final report Sentiment_and_Topic_Analysis_Presentation.pdf: pdf of final presentation Sentiment_and_Topic_Analysis_Presentation.pptx: PowerPoint of final presentation Sentiment_and_Topic_Analysis_LaTeX.zip: zip file of LaTeX files used to write final report Sentiment_and_Topic_Analysis_Work_Files.tar: source code and data files, contents listed below: AT0412.txt: tested dataset Word2VecSentimentAnalysis.scala: sentiment classifier topics_1 through topics_4: result files Topic analysis/MainWindow.scala: UI code Topic analysis:/pom.xml: used for UI Sentiment analysis/final_sentiment_analysis.py: reads tweet collection for sentiment analysis Sentiment analysis/first3.sh: passes tweet into syntaxnet Sentiment analysis/parse_tree.py: renders parse tree to represent file returned by syntaxnet Sentiment analysis/reverse_polarity_file:polarity reversal and negation words from General Inquirer | en |
| dc.description.sponsorship | NSF: IIS-1319578 | en |
| dc.description.sponsorship | NSF: IIS-1619028 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/77883 | en |
| dc.language.iso | en_US | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution 3.0 United States | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | en |
| dc.subject | topic analysis | en |
| dc.subject | sentiment analysis | en |
| dc.subject | tweets | en |
| dc.subject | natural language processing | en |
| dc.subject | nlp | en |
| dc.subject | linguistic analysis | en |
| dc.title | Sentiment and Topic Analysis | en |
| dc.type | Dataset | en |
| dc.type | Presentation | en |
| dc.type | Report | en |
| dc.type | Software | en |
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