【摘要】：該文以一種有效的方法尋找出有價(jià)值的微博評(píng)論,這對(duì)于讀者更高效地閱讀評(píng)論,為輿情分析、文本挖掘等任務(wù)提供支持,均具有重要的應(yīng)用價(jià)值。針對(duì)微博及其評(píng)論文本短小、內(nèi)容發(fā)散等特點(diǎn),該文提出一種基于無(wú)指導(dǎo)學(xué)習(xí)的微博評(píng)論分析方法,該方法通過(guò)互聯(lián)網(wǎng)搜索引擎擴(kuò)展微博文本,基于相關(guān)性計(jì)算自動(dòng)構(gòu)造正負(fù)訓(xùn)練用例,生成特定的某條微博評(píng)論分類模型,通過(guò)該模型對(duì)評(píng)論的價(jià)值性進(jìn)行評(píng)估。實(shí)驗(yàn)結(jié)果表明,該方法能夠比較好地識(shí)別出評(píng)論的價(jià)值。
[Abstract]:This paper uses an effective method to find valuable Weibo comments, which has important application value for readers to read comments more efficiently, to provide support for public opinion analysis, text mining and other tasks. In view of the characteristics of Weibo and his comments, such as short text and divergent content, this paper proposes an analysis method of Weibo's comments based on unguided learning. This method extends Weibo's text through the Internet search engine. The positive and negative training cases are automatically constructed based on the correlation calculation, and a specific Weibo comment classification model is generated, through which the value of comments is evaluated. The experimental results show that the method can recognize the value of comments.
【作者單位】： 南京大學(xué)計(jì)算機(jī)軟件新技術(shù)國(guó)家重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金(61170181) 江蘇省自然科學(xué)基金(BK2011192) 國(guó)家社會(huì)科學(xué)基金(11AZD121)
【分類號(hào)】：TP391.1

【相似文獻(xiàn)】

相關(guān)期刊論文 前1條

1 李東明;張麗娟;趙偉;石晶;;無(wú)指導(dǎo)學(xué)習(xí)語(yǔ)義優(yōu)選[J];計(jì)算機(jī)應(yīng)用與軟件;2012年01期

，

本文編號(hào)：2208747