【摘要】：文本情感分析是多媒體智能理解的重要問題之一.情感分類是情感分析領(lǐng)域的核心問題,旨在解決評論情感極性的自動判斷問題.由于互聯(lián)網(wǎng)評論數(shù)據(jù)規(guī)模與日俱增,傳統(tǒng)基于詞典的方法和基于機(jī)器學(xué)習(xí)的方法已經(jīng)不能很好地處理海量評論的情感分類問題.隨著近年來深度學(xué)習(xí)技術(shù)的快速發(fā)展,其在大規(guī)模文本數(shù)據(jù)的智能理解上表現(xiàn)出了獨(dú)特的優(yōu)勢,越來越多的研究人員青睞于使用深度學(xué)習(xí)技術(shù)來解決文本分類問題.主要分為2個部分:1)歸納總結(jié)傳統(tǒng)情感分類技術(shù),包括基于字典的方法、基于機(jī)器學(xué)習(xí)的方法、兩者混合方法、基于弱標(biāo)注信息的方法以及基于深度學(xué)習(xí)的方法;2)針對前人情感分類方法的不足,詳細(xì)介紹所提出的面向情感分類問題的弱監(jiān)督深度學(xué)習(xí)框架.此外,還介紹了評論主題提取相關(guān)的經(jīng)典工作.最后,總結(jié)了情感分類問題的難點(diǎn)和挑戰(zhàn),并對未來的研究工作進(jìn)行了展望.
[Abstract]:Text emotion analysis is one of the most important problems in multimedia intelligent understanding. Emotion classification is a core problem in the field of emotion analysis, which aims to solve the problem of automatic judgment of emotional polarity. Due to the increasing scale of Internet comment data, the traditional dictionary-based approach and the machine-learning approach can not deal with the emotional classification of mass reviews. With the rapid development of deep learning technology in recent years, it has shown a unique advantage in the intelligent understanding of large-scale text data. More and more researchers prefer to use depth learning technology to solve text classification problems. It is divided into two parts: 1) generalize and summarize the traditional emotion classification technology, including dictionary-based method, machine-learning based method, and hybrid method. Aiming at the shortcomings of previous affective classification methods, a weak supervised depth learning framework for affective classification problems is introduced in detail. In addition, the classic work related to topic extraction of comments is also introduced. Finally, the difficulties and challenges of affective classification are summarized, and the future research work is prospected.
【作者單位】： 西北大學(xué)信息科學(xué)與技術(shù)學(xué)院;
【基金】：國家自然科學(xué)基金優(yōu)秀青年科學(xué)基金項目(61522206)~~
【分類號】：TP181;TP391.1
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本文編號：2214104