کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6855355 1437612 2018 32 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Opinion mining using ensemble text hidden Markov models for text classification
ترجمه فارسی عنوان
معادله نظر با استفاده از متن گروه مارکف پنهان برای طبقه بندی متن
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
With the rapid growth of social media, text mining is extensively utilized in practical fields, and opinion mining, also known as sentiment analysis, plays an important role in analyzing opinion and sentiment in texts. Methods in opinion mining generally depend on a sentiment lexicon, which is a set of predefined key words that express sentiment. Opinion mining requires proper sentiment words to be extracted in advance and has difficulty classifying sentences that imply an opinion without using any sentiment key words. This paper presents a new sentiment analysis method, based on text-based hidden Markov models (TextHMMs), for text classification that uses a sequence of words in training texts instead of a predefined sentiment lexicon. We sought to learn text patterns representing sentiment through ensemble TextHMMs. Our method defines hidden variables in TextHMMs by semantic cluster information in consideration of the co-occurrence of words, and thus calculates the sentiment orientation of sentences by fitted TextHMMs. To reflect diverse patterns, we applied an ensemble of TextHMM-based classifiers. In the experiments with a benchmark data set, we show that this method is superior to some existing methods and particularly has potential to classify implicit opinions. We also demonstrate the practicality of the proposed method in a real-life data set of online market reviews.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 94, 15 March 2018, Pages 218-227
نویسندگان
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