Методы оценки эмоциональной окраски текста

Автор: Ермаков Сергей Александрович, Ермакова Лиана Магдановна

Журнал: Вестник Пермского университета. Серия: Математика. Механика. Информатика @vestnik-psu-mmi

Рубрика: Информатика. Информационные системы

Статья в выпуске: 1 (9), 2012 года.

Бесплатный доступ

Проводится обзор существующих методов определения эмоциональной окраски текста. Осо- бое внимание уделяется методам построения интегральной оценки на основе коллекции до- кументов, содержащих большое количество избыточной и противоречивой информации.

Сентимент-анализ, анализ тональности текста, машинное обучение, графовые модели, эмотивная лексика

Короткий адрес: https://sciup.org/14729777

IDR: 14729777

Список литературы Методы оценки эмоциональной окраски текста

  • Gamon M., et al. Pulse: Mining Customer Opinions from Free Text//Proceedings of the 6th International Symposium on Intelligent Data Analysis (IDA). 2005. P.121-132.
  • Wiebe J., Wilson T., Cardie C. Annotating Expressions of Opinions and Emotions in Language//Proceedings of Language
  • Пазельская А., Соловьев А. Метод определения эмоций в текстах на русском языке: труды международной конференции "Диалог, 2011". P.510-522.
  • "Дорожки РОМИП'2011" Available: http://romip.ru/ru/2011/tracks.html. [Дата обращения: 15.11.2011].
  • Ng R., Pauls A. Multi-document summarization of evaluative text//Proceedings of the 11st Conference of the European Chapter of the Association for Computational Linguistics. 2006. P.305-312.
  • Carenini G., Ng R. Zwart E. Extracting knowledge from evaluative text//Proceedings of the 3rd international conference on Knowledge capture. 2005. P.11-18.
  • Hu M., Liu B. Mining and summarizing customer reviews//Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. 2004. P. 168-177.
  • Snyder B., Barzilay R. Multiple Aspect Ranking using the Good Grief Algorithm//Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference HLTNAACL. 2007. P. 300-307.
  • Lerman K., Blair-Goldensohn S., Mcdonald R. Sentiment summarization: evaluating and learning user preferences//Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics. 2009.
  • Lu Y., Zhai C., Sundaresan N. Rated aspect summarization of short comments//Proceedings of the 18th international conference on World wide web. 2009. P.131-140.
  • Titov I., Mcdonald R. A Joint Model of Text and Aspect Ratings for Sentiment Summarization//Proceedings of ACL-08: HLT. 2008. P. 308-316.
  • Pang B., Lee L. Opinion Mining and Sentiment Analysis. 2008. P. 1-135.
  • Pang B., Lee L., Vaithyanathan S. Thumbs up? Sentiment Classification using Machine Learning Techniques // Proceedings of the Conference on Machine Learning Techniques // Proceedings of the Conference on Emprical Methods in Natural Language Processing. 2002. P. 79-86.
  • Turney P. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews//Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. 2002. P. 417-424.
  • Wiebe J., Bruce R., O'Hara T. Development and use of a gold-standard data set for subjectivity classifications//Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics. 1999. P. 246-253.
  • Kim S.-M., Hovy E. Identifying and Analyzing Judgment Opinions//Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, 2006, P. 200-207.
  • Yu H., Hatzivassiloglou V., Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences//Proceedings of the 2003 conference on Empirical methods in natural language processing, 2003, P. 79-86.
  • Pang B., Lee L. A Sentimental Education: Sentiment Analysis Using Subjectivity//Proceedings of the ACL, 2004, P. 271-278.
  • Wiebe J., Wilson T., Bell M. Identifying Collocations for Recognizing Opinions//Proc. ACL/EACL 01 Workshop on Collocation, 2001.
  • Павлов А., Добров Б. Метод обнаружения массово порожденных неестественных текстов на основе анализа тематической структуры//Вычислительные методы и программирование, 2011, T. 12, P. 58-72,.
  • Blei D., Ng A., Jordan M. Latent Dirichlet Allocation//Journal of Machine Learning Research, 2003, № 3, P. 993-1022.
  • Chetviorkin I., Loukachevitch N. Threeway movie review classification//Proceedings of international conference Dialog, 2011, P. 168-177.
  • Ganesan K., Zhai C, Han J. Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions//Proceedings of the 23rd International Conference on Computational Linguistics. 2010. P.340 348
  • Radev D., McKeown K. Generating natural language summaries from multiple on-line sources//Computational Linguistics -Special issue on natural language generation. 1998. Vol.24. №3. P.469-500
  • Harabagiu S., Lacatusu F. Generating Single and Multi-Document Summaries with GISTEXTER//Document Understanding Conference. 2002.
  • Saggion K, Lapalme G. Generating Indicative-Informative Summaries with SumUM//Association for Computational Linguistics. 2002.
  • Jing H., McKeow K. Cut and paste based text summarization//Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference. 2002. P. 178-185.
  • Barzilay R, Lee L. Learning to Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment//NAACLHLT. 2003. P. 16-23.
  • Aue A., Ganion M., Customizing Sentiment Classifiers to New Domains: a Case Study//Proceedings of Recent Advances in Natural Language Processing (RANLP-2005). 2005. Vol. 49. № 2
  • Dietterich T. Macine learning research: Four current directions//AI Magazine. 1997, Vol. 18, № 4. P. 97-136
  • Todorovski L.f Dzeroski S., Combining classifiers with meta decision trees//Machine Learning. 2003. Vol. 50. № 3. P. 223-249.
  • Nigam C, McCalhtm A., Thrun. S. Text classification from labeled and unlabeled documents//Machine Learning,. 2000, Vol. 39. № 2. P. 103-134
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