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Sentiment Analysis in A Cross-Media Analysis Framework
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2016 (English)In: PROCEEDINGS OF 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA ANALYSIS (ICBDA), 2016, 27-31 p.Conference paper, (Refereed)
Abstract [en]

This paper introduces the implementation and integration of a sentiment analysis pipeline into the ongoing open source cross-media analysis framework. The pipeline includes the following components; chat room cleaner, NLP and sentiment analyzer. Before the integration, we also compare two broad categories of sentiment analysis methods, namely lexicon-based and machine learning approaches. We mainly focus on finding out which method is appropriate to detect sentiments from forum discussion posts. In order to conduct our experiments, we use the apache-hadoop framework with its lexicon-based sentiment prediction algorithm and Stanford coreNLP library with the Recursive Neural Tensor Network (RNTN) model. The lexicon-based uses sentiment dictionary containing words annotated with sentiment labels and other basic lexical features, and the later one is trained on Sentiment Treebank with 215,154 phrases, labeled using Amazon Turk. Our overall performance evaluation shows that RNTN outperforms the lexicon-based by 9.88% accuracy on variable length positive, negative, and neutral comments. How-ever, the lexicon-based shows better performance on classifying positive comments. We also found out that the F1-score values of the Lexicon-based is greater by 0.16 from the RNTN.

Place, publisher, year, edition, pages
2016. 27-31 p.
Keyword [en]
sentiment analysis, cross-media, machine learning algorithm, lexicon-based, neural network
National Category
Information Systems
Identifiers
URN: urn:nbn:se:umu:diva-130264DOI: 10.1109/ICBDA.2016.7509790ISI: 000390299100006ISBN: 978-1-4673-9591-5 (print)OAI: oai:DiVA.org:umu-130264DiVA: diva2:1065296
Conference
IEEE International Conference on Big Data Analysis (ICBDA), MAR 12-14, 2016, Hangzhou, PEOPLES R CHINA
Available from: 2017-01-14 Created: 2017-01-14 Last updated: 2017-01-14Bibliographically approved

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Woldemariam, Yonas
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf