کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4972631 | 1365427 | 2016 | 31 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Social emotion classification of short text via topic-level maximum entropy model
ترجمه فارسی عنوان
طبقه بندی احساسات اجتماعی از متن کوتاه با استفاده از مدل آنتروپی حداکثر در سطح موضوع
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کلمات کلیدی
مدل حداکثر آنتروپی سطح در سطح موضوع، طبقه بندی احساسات اجتماعی، تجزیه و تحلیل متن کوتاه، معاینه عمومی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
سیستم های اطلاعاتی
چکیده انگلیسی
With the rapid proliferation of Web 2.0, the identification of emotions embedded in user-contributed comments at the social web is both valuable and essential. By exploiting large volumes of sentimental text, we can extract user preferences to enhance sales, develop marketing strategies, and optimize supply chain for electronic commerce. Pieces of information in the social web are usually short, such as tweets, questions, instant messages, messages, and news headlines. Short text differs from normal text because of its sparse word co-occurrence patterns, which hampers efforts to apply social emotion classification models. Most existing methods focus on either exploiting the social emotions of individual words or the association of social emotions with latent topics learned from normal documents. In this paper, we propose a topic-level maximum entropy (TME) model for social emotion classification over short text. TME generates topic-level features by modeling latent topics, multiple emotion labels, and valence scored by numerous readers jointly. The overfitting problem in the maximum entropy principle is also alleviated by mapping the features to the concept space. An experiment on real-world short documents validates the effectiveness of TME on social emotion classification over sparse words.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information & Management - Volume 53, Issue 8, December 2016, Pages 978-986
Journal: Information & Management - Volume 53, Issue 8, December 2016, Pages 978-986
نویسندگان
Yanghui Rao, Haoran Xie, Jun Li, Fengmei Jin, Fu Lee Wang, Qing Li,