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By Bo Pang, Lillian Lee

Важная часть сбора информации всегда заключалась в том, чтобы выяснить, что думают другие люди. С растущей популярностью ресурсов, содержащих мнения и отзывы, таких как личные блоги или сайты онлайн-обзоров, возникают как новые возможности, так и новые трудности в этой сфере, поскольку люди активно используют ИТ для поиска мнений. Внезапная активность в области извлечения мнений и смыслового анализа, которая занимается автоматической обработкой мнений и субъективности текстов, отчасти возникла в ответ на волну интереса к мнениям как к первостепенному объекту исследования. В книге "Opinion Mining And Sentiment research" рассматриваются технологии и подходы, используемые при разработке поисковых систем, ориентированных на мнения. Книга также включает описание различных программ и приложений, нацеленных на поиск, извлечение и классификацию мнений. Кроме технических вопросов авторы затрагивают более широкие проблемы, такие как приватность, манипуляция мнениями и их возможное экономическое влияние.
"Opinion Mining and Sentiment research" - это полное всесторонее описание данной области, которое будет интересно широкому кругу читателей: студентам, ученым, разработчикам.


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The New York Times vs. The Daily News); the work of Kessler et al. , high-brow vs. “popular,” or low-brow) has similar goals. Several recent workshops have been dedicated to style analysis in text [15, 16, 17]. Determining stylistic characteristics can be useful in multifaceted search [10]. Another problem that has been considered in intelligence and security settings is the detection of deceptive language [46, 117, 329]. 2 Features Converting a piece of text into a feature vector or other representation that makes its most salient and important features available is an important part of data-driven approaches to text processing.

5 for more detail). For instance, Yu and Hatzivassiloglou [326] achieve high accuracy (97%) with a Naive Bayes classifier on a particular corpus consisting of Wall Street Journal articles, where the task is to distinguish articles under News and Business (facts) from articles under Editorial and Letter to the Editor (opinions). (This task was suggested earlier by Wiebe et al. ) Work in this direction is not limited to the binary distinction between subjective and objective labels. Recent work includes the research by participants in the 2006 TREC Blog track [227] and others [69, 97, 222, 223, 234, 279, 316, 326].

Since subjective genres, such as “editorial,” are often one of the possible categories, such work can be viewed as closely related to subjectivity detection. Indeed, this relation has been observed in work focused on learning subjective language [316]. There has also been research that concentrates on classifying documents according to their source or source style, with statistically detected stylistic variation [38] serving as an important cue. Authorship identification is perhaps the most salient example — Mosteller and Wallace’s [216] classic Bayesian study of the authorship of the Federalist Papers is one well-known instance.

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