本文選題：情感挖掘 + 短文本　； 參考：《計(jì)算機(jī)科學(xué)》2017年10期

【摘要】：針對(duì)中文短文本信息量少、特征稀疏等特點(diǎn),面向微博短文本進(jìn)行情感分類研究,為了更好地提取短文本情感特征,從評(píng)論轉(zhuǎn)發(fā)等上下文內(nèi)容中挖掘具有語義遞進(jìn)關(guān)系的語料對(duì)原文本進(jìn)行擴(kuò)展,并抽取具有潛在感情色彩的特征詞,采用Word2vec計(jì)算詞語相似度以進(jìn)行候選特征詞擴(kuò)展,最后引入深度信念網(wǎng)絡(luò)(Deep Belief Network,DBN)對(duì)候選特征詞進(jìn)行深度自適應(yīng)學(xué)習(xí)。在COAE(Chinese Opinion Analysis Evaluation)2015任務(wù)評(píng)測數(shù)據(jù)集上的實(shí)驗(yàn)表明,該方法能夠有效地緩解短文本特征稀疏問題,并且能夠較為準(zhǔn)確地挖掘情感特征,提高情感分類的準(zhǔn)確率。
[Abstract]:In view of the characteristics of Chinese short text, such as less information and sparse features, the emotion classification research is carried out for Weibo short text, in order to extract the emotion feature of short text better, The original text is extended by mining the corpus with semantic progressive relation from the context such as comment and forwarding, and the feature words with potential emotional color are extracted. Word2vec is used to calculate the similarity of words in order to extend the candidate feature. Finally, a deep belief network (Deep Belief Network DBN) is introduced to study candidate features in depth adaptive learning. Experiments on the COAE(Chinese Opinion Analysis Evaluation)2015 task evaluation data set show that the proposed method can effectively alleviate the sparse problem of short text features and can mine emotional features accurately and improve the accuracy of emotion classification.
【作者單位】： 北京工業(yè)大學(xué)計(jì)算機(jī)學(xué)院;
【基金】：國家科技支撐計(jì)劃子課題(2013BAH21B02-01) 北京市自然科學(xué)基金資助項(xiàng)目(4153058) 上海市智能信息處理重點(diǎn)實(shí)驗(yàn)室開放基金(IIPL-2014-004)資助
【分類號(hào)】：TP391.1

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本文編號(hào)：1930423