Multilingual Sentiment Analysis on Social Media | An Extensive Study on Multilingual Sentiment Analaysis Performed on Three Different Social Media

Erik Tromp

ISBN 10: 3659135615 / ISBN 13: 9783659135613

Published by LAP LAMBERT Academic Publishing, 2012

About this Item

Multilingual Sentiment Analysis on Social Media | An Extensive Study on Multilingual Sentiment Analaysis Performed on Three Different Social Media | Erik Tromp | Taschenbuch | 120 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659135613 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.

Seller Inventory # 106437548

Bibliographic Details

• Title: Multilingual Sentiment Analysis on Social Media | An Extensive Study on Multilingual Sentiment Analaysis Performed on Three Different Social Media
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2012
• Language: English
• ISBN 10: 3659135615
• ISBN 13: 9783659135613
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 8D millimeters 220 millimeters width by 150 millimeters height by 8 millimeters depth
• Weight: 197 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

As social media more and more connect the entire world, there is an increasing importance to analyze multilingual data rather than unilingual data. The automated sentiment analysis we perform extracts opinions from the relatively short messages placed on social media in multiple languages. --- We present a four-step approach to perform sentiment analysis. Our approach comprises language identification, part-of-speech tagging, subjectivity detection and polarity detection. For language identification we propose an algorithm we call LIGA which captures grammar of languages in addition to occurrences of characteristics. For part-of-speech tagging we use an existing solution called the TreeTagger, developed at the University of Stuttgart. We apply AdaBoost using decision stumps to solve subjectivity detection. For polarity detection we propose an algorithm we call RBEM which uses heuristic rules to create an emissive model on patterns.

About the Author

Erik Tromp is fond of developing new methods to perform analytics on natural language. He received his Msc degree cum laude at the Eindhoven University of Technology where he heavily studied data mining and analytics. More specifically, he focused on natural language processing. His work on sentiment analysis has been honored with multiple awards.