【摘要】：該文以一種有效的方法尋找出有價值的微博評論,這對于讀者更高效地閱讀評論,為輿情分析、文本挖掘等任務提供支持,均具有重要的應用價值。針對微博及其評論文本短小、內容發(fā)散等特點,該文提出一種基于無指導學習的微博評論分析方法,該方法通過互聯(lián)網搜索引擎擴展微博文本,基于相關性計算自動構造正負訓練用例,生成特定的某條微博評論分類模型,通過該模型對評論的價值性進行評估。實驗結果表明,該方法能夠比較好地識別出評論的價值。
[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】： 南京大學計算機軟件新技術國家重點實驗室;
【基金】：國家自然科學基金(61170181) 江蘇省自然科學基金(BK2011192) 國家社會科學基金(11AZD121)
【分類號】：TP391.1

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本文編號：2208747