کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6862555 677285 2014 25 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Crowd explicit sentiment analysis
ترجمه فارسی عنوان
تجزیه و تحلیل احساسات صریح جمعیت
کلمات کلیدی
تجزیه و تحلیل احساسات، طبقه بندی قطبی، مدل های احساسی، رسانه های اجتماعی، فیلتر کردن داده های بزرگ، کسب دانش، تولید دانش دانش،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
With the rapid growth of data generated by social web applications new paradigms in the generation of knowledge are opening. This paper introduces Crowd Explicit Sentiment Analysis (CESA) as an approach for sentiment analysis in social media environments. Similar to Explicit Semantic Analysis, microblog posts are indexed by a predefined collection of documents. In CESA, these documents are built up from common emotional expressions in social streams. In this way, texts are projected to feelings or emotions. This process is performed within a Latent Semantic Analysis. A few simple regular expressions (e.g. “I feel X”, considering X a term representing an emotion or feeling) are used to scratch the enormous flow of micro-blog posts to generate a textual representation of an emotional state with clear polarity value (e.g. angry, happy, sad, confident, etc.). In this way, new posts can be indexed by these feelings according to the distance to their textual representation. The approach is suitable in many scenarios dealing with social media publications and can be implemented in other languages with little effort. In particular, we have evaluated the system on Polarity Classification with both English and Spanish data sets. The results show that CESA is a valid solution for sentiment analysis and that similar approaches for model building from the continuous flow of posts could be exploited in other scenarios.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 69, October 2014, Pages 134-139
نویسندگان
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