發(fā)布時間：2019-01-23 18:23

【摘要】：越來越多的用戶喜歡通過微博來實時分享自己的觀點或者表達自己的情感,因此,面向微博的意見挖掘或情感分析成為了研究熱點。其中,微博主客觀分類研究是意見挖掘和情感分析研究的基礎(chǔ),其主要任務(wù)是區(qū)分微博中表達主觀觀點的文本和表達客觀事實的文本,并進一步從主觀性文本中挖掘潛在有價值的信息。此外,面向微博的主客觀分類研究對意見問答系統(tǒng)、觀點摘要等工作也具有重要意義。本文針對中文微博的主客觀分類問題,分別研究了語法和語義特征結(jié)合不同特征選擇方法對微博主客觀分類的影響,同時對基于融合特征的微博主客觀分類方法進行了探索性研究。本文主要研究成果如下：(1)針對語法特征,提出了基于2-gram的詞、詞性特征的提取算法。本文借鑒2-gram模型分別提取了微博文本的連續(xù)雙詞(2-word)、連續(xù)雙詞詞性(2-pos)組合模式特征作為語法特征來進行微博主客觀分類研究。(2)針對語義特征,充分考慮了情感分析經(jīng)驗以及微博文本特點,提出了微博內(nèi)容特征、比重特征等豐富的語義特征,并引入了微博文本情感詞庫來進行微博主客觀分類研究。(3)針對微博文本特征選擇問題,分別對兩類特征選擇方法進行了分類性能比較。本文分別利用不同特征選擇方法對語法和語義特征進行評估來獲取最優(yōu)特征集,并結(jié)合分類模型對分類效果進行了比較。(4)針對微博主客觀分類問題,提出了一種基于融合特征的微博主客觀分類方法。該方法通過設(shè)計特征融合算法對不同特征選擇方法進行有效組合來獲取融合特征,并結(jié)合機器學習方法來進行微博主客觀分類研究。本文研究構(gòu)建了更豐富的主客觀分類特征,并設(shè)計了一種特征融合算法來探索特征選擇方法組合后對主客觀分類效果的影響。實驗證明,本文提出的特征融合算法可以有效提高主客觀分類效果,同時構(gòu)建了相對通用的主客觀分類模型。
[Abstract]:More and more users like to share their views or express their emotions through Weibo in real time. Among them, Weibo's subjective and objective classification is the basis of opinion mining and emotional analysis, and its main task is to distinguish the text expressing subjective views from the text expressing objective facts in Weibo. And further mining the potentially valuable information from the subjective text. In addition, the subjective and objective classification research for Weibo is also of great significance to the quizzes and abstracts of opinions. Aiming at the subjective and objective classification of Chinese Weibo, this paper studies the influence of grammar and semantic features combined with different feature selection methods on the subjective and objective classification of Weibo. At the same time, the subjective and objective classification method of Weibo based on fusion features is studied. The main results of this paper are as follows: (1) an algorithm of extracting word and part of speech features based on 2-gram is proposed for grammatical features. This paper draws on the 2-gram model to extract the 2-word of Weibo's text and the combination pattern feature of continuous two-word (2-pos) as grammatical features. (2) aiming at the semantic features, we study the subjective and objective categorization of Weibo. Having fully considered the experience of emotional analysis and the characteristics of Weibo's text, we put forward the abundant semantic features such as the content features and specific weight features of Weibo. Weibo text emotion thesaurus is introduced to study the subjective and objective classification of Weibo. (3) two feature selection methods are compared in order to solve the feature selection problem. In this paper, different feature selection methods are used to evaluate the grammar and semantic features to obtain the optimal feature collection, and the classification effect is compared with the classification model. (4) aiming at the objective and subjective classification problem of Weibo, This paper presents a subjective and objective classification method for Weibo based on fusion features. In this method, the feature fusion algorithm is designed to effectively combine different feature selection methods to obtain the fusion features, and the machine learning method is used to study Weibo's subjective and objective classification. In this paper, a more abundant subjective and objective classification features are constructed, and a feature fusion algorithm is designed to explore the effect of the combination of feature selection methods on the subjective and objective classification results. Experimental results show that the proposed feature fusion algorithm can effectively improve the subjective and objective classification effect, and build a relatively common subjective and objective classification model.
【學位授予單位】：山西大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP391.1;TP393.092

【參考文獻】

相關(guān)碩士學位論文 前1條

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本文編號：2414082