Opinion Mining on a German Corpus of a Media Response Analysis

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• 作者：Thomas Scholz (1) scholz@cs.uni-duesseldorf.de
  Stefan Conrad (1) conrad@cs.uni-duesseldorf.de
  Lutz Hillekamps (2) lutz.hillekamps@pressrelations.de
• 关键词：Corpora and Language Resources – ; Media Response Analysis – ; Sentiment Analysis – ; Opinion Extraction – ; Viewpoint Determination
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7499
• 期：1
• 页码：39-46
• 全文大小：169.7 KB
• 参考文献：1. Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. In: Proc. of the 27th Annual Meeting of the ACL, ACL 1989, pp. 76–83 (1989)
  2. Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proc. of the Intl. Conf. on Web Search and Web Data Mining, WSDM 2008, pp. 231–240 (2008)
  3. Greene, S., Resnik, P.: More than words: syntactic packaging and implicit sentiment. In: Proc. of Human Language Technologies: The 2009 Annual Conf. of the North American Chapter of the ACL, NAACL 2009, pp. 503–511 (2009)
  4. Harb, A., Planti茅, M., Dray, G., Roche, M., Trousset, F., Poncelet, P.: Web opinion mining: how to extract opinions from blogs? In: Proc. of the 5th Intl. Conf. on Soft Computing as Transdisciplinary Science and Technology, CSTST 2008, pp. 211–217 (2008)
  5. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proc. of the 10th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, KDD 2004, pp. 168–177 (2004)
  6. Kaji, N., Kitsuregawa, M.: Building lexicon for sentiment analysis from massive collection of html documents. In: Proc. of the 2007 Joint Conf. on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL (2007)
  7. Momtazi, S.: Fine-grained german sentiment analysis on social media. In: Proc. of the 9th Intl. Conf. on Language Resources and Evaluation, LREC 2012 (2012)
  8. Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proc. of the 42nd Meeting of the ACL, pp. 271–278 (2004)
  9. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)
  10. Park, S., Lee, K., Song, J.: Contrasting opposing views of news articles on contentious issues. In: Proc. of the 49th Annual Meeting of the ACL: Human Language Technologies, HLT 2011, vol. 1, pp. 340–349 (2011)
  11. Remus, R., Quasthoff, U., Heyer, G.: Sentiws – a publicly available German-language resource for sentiment analysis. In: Proc. of the 7th Intl. Conf. on Language Resources and Evaluation, LREC 2010 (2010)
  12. Sarvabhotla, K., Pingali, P., Varma, V.: Sentiment classification: a lexical similarity based approach for extracting subjectivity in documents. Inf. Retr. 14(3), 337–353 (2011)
  13. Somasundaran, S., Wiebe, J.: Recognizing stances in ideological on-line debates. In: Proc. of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, CAAGET 2010, pp. 116–124 (2010)
  14. Watson, T., Noble, P.: Evaluating public relations: a best practice guide to public relations planning, research & evaluation. PR in practice series, ch. 6, pp. 107–138. Kogan Page (2007)
  15. Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Language Resources and Evaluation 39(2-3), 165–210 (2005)
  16. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Linguistics 35(3), 399–433 (2009)
• 作者单位：1. Institute of Computer Science, Heinrich-Heine-University, D眉sseldorf, Germany2. Editorial Department & Media Analysis, pressrelations, D眉sseldorf, Germany
• ISSN：1611-3349

文摘

This contribution introduces a new corpus of a German Media Response Analysis called the pressrelations dataset which can be used in several tasks of Opinion Mining: Sentiment Analysis, Opinion Extraction and the determination of viewpoints. Professional Media Analysts created a corpus of 617 documents which contains 1,521 statements. The statements are annotated with a tonality (positive, neutral, negative) and two different viewpoints. In our experiments, we perform sentiment classifications by machine learning techniques which are based on different methods to calculate tonality.