本文選題：校園 + BBS��； 參考：《福州大學(xué)》2014年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)的發(fā)展,校園BBS已經(jīng)成為了大學(xué)生網(wǎng)上交流的主要平臺(tái)之一通過校園BBS輿情監(jiān)測(cè),可以從校園BBS中采集數(shù)據(jù)并對(duì)數(shù)據(jù)進(jìn)行分析處理,根據(jù)分析結(jié)果進(jìn)行準(zhǔn)確的校園輿情狀態(tài)控制、校園輿情發(fā)現(xiàn)與預(yù)警等相關(guān)操作。本文分析了校園BBS輿情監(jiān)測(cè)的整體組織框架,將校園BBS輿情分析系統(tǒng)分成四個(gè)模塊,分別是論壇數(shù)據(jù)采集模塊、數(shù)據(jù)預(yù)處理模塊、輿情信息分析模塊和輿情監(jiān)測(cè)與服務(wù)模塊。本文分別介紹了在這四個(gè)模塊中用到的一些常用的技術(shù),主要有基于校園BBS的聚焦爬蟲算法和改進(jìn)技術(shù)、針對(duì)BBS邏輯結(jié)構(gòu)分析的帖子去噪技術(shù),以及中文分詞技術(shù),停用詞技術(shù)、文本分類方法、關(guān)鍵詞提取算法等。本文重點(diǎn)研究了校園BBS輿情監(jiān)測(cè)中的信息分析和輿情服務(wù)模塊,主要有：1、改良的觀點(diǎn)傾向識(shí)別技術(shù)在觀點(diǎn)傾向識(shí)別技術(shù)研究中,分析了傳統(tǒng)的識(shí)別技術(shù),并研究了基于HOWNET詞庫的觀點(diǎn)傾向性識(shí)別,針對(duì)回帖一般字?jǐn)?shù)較少的特點(diǎn)還對(duì)回帖采用了計(jì)算否定詞數(shù)量的觀點(diǎn)傾向性識(shí)別算法改進(jìn)。2、基于k-means算法的校園BBS聚類算法通過計(jì)算兩個(gè)帖子關(guān)鍵詞間的余弦相似度,可以得到兩個(gè)帖子之間的距離(相似度),通過k-means算法將各個(gè)帖子按各自的距離進(jìn)行聚類,能夠得到較好的效果。3、基于關(guān)聯(lián)規(guī)則自動(dòng)更新的知識(shí)庫匹配下的敏感帖監(jiān)測(cè)技術(shù)敏感帖識(shí)別一般是基于知識(shí)規(guī)則庫進(jìn)行匹配,知識(shí)規(guī)則庫中敏感詞匯的更新是影響其性能的主要因素之一。當(dāng)發(fā)現(xiàn)一個(gè)帖子是敏感帖后,本文還對(duì)這個(gè)帖子的其它關(guān)鍵字進(jìn)行記錄與分析,通過關(guān)聯(lián)規(guī)則算法,將能夠滿足最小支持度閥值和最小置信度閥值的關(guān)鍵字收納入知識(shí)規(guī)則庫,實(shí)現(xiàn)了知識(shí)規(guī)則庫的自動(dòng)更新,經(jīng)實(shí)驗(yàn)證明此方法支持下的敏感帖識(shí)別具有較高的準(zhǔn)確率,并且能夠隨著識(shí)別次數(shù)的增加而提高識(shí)別準(zhǔn)確率。4、熱點(diǎn)話題識(shí)別與預(yù)測(cè)技術(shù)本文重點(diǎn)研究了熱點(diǎn)話題識(shí)別與預(yù)測(cè)技術(shù)。其中在話題識(shí)別中針對(duì)校園BBS的特點(diǎn)提出了基于話題發(fā)帖量、話題瀏覽數(shù)、話題回復(fù)數(shù)以及話題精華帖量四個(gè)參數(shù)綜合計(jì)算的話題識(shí)別方法。在熱點(diǎn)話題預(yù)測(cè)中,研究了基于貝葉斯網(wǎng)絡(luò)的熱點(diǎn)話題預(yù)測(cè)技術(shù),通過計(jì)算某個(gè)話題成為熱點(diǎn)話題的可能性,成為普通話題的可能性以及成為冷門話題的可能性來預(yù)測(cè)該話題的輿情走向。通過這些研究,能夠提高校園BBS輿情監(jiān)測(cè)的效率,為校園BBS輿情監(jiān)測(cè)提供技術(shù)支持。
[Abstract]:With the development of Internet, campus BBS has become one of the main platforms for college students to communicate online. Through the monitoring of campus BBS public opinion, we can collect data from campus BBS and analyze and process the data.According to the results of the analysis of accurate campus public opinion state control, campus public opinion discovery and early warning and other related operations.This paper analyzes the overall organizational framework of campus BBS public opinion monitoring. The campus BBS public opinion analysis system is divided into four modules: forum data acquisition module, data preprocessing module, public opinion information analysis module and public opinion monitoring and service module.This paper introduces some common techniques used in these four modules, mainly focused crawler algorithm and improved technology based on campus BBS, post denoising technology for BBS logical structure analysis, and Chinese word segmentation technology.Stop word technology, text classification method, keyword extraction algorithm and so on.This paper focuses on the research of information analysis and public opinion service module in campus BBS public opinion monitoring.And the view orientation recognition based on HOWNET thesaurus is studied.In view of the small number of words in general, the paper also uses an improved algorithm of attitude orientation recognition to calculate the number of negative words. The campus BBS clustering algorithm based on k-means algorithm calculates the cosine similarity between the keywords of two posts.We can get the distance between two posts (similarity degree), and cluster each post according to their own distance by k-means algorithm.It can get a good result. 3. Sensitive post recognition is based on knowledge rule base, which is based on the knowledge base matching, which is based on automatic updating of association rules.The update of sensitive words in the knowledge rule base is one of the main factors affecting its performance.When a post is found to be sensitive, this paper also records and analyzes the other keywords of this post. By using association rule algorithm, the key words that can satisfy the minimum support threshold and the minimum confidence threshold are incorporated into the knowledge rule base.The automatic updating of knowledge rule base is realized. It is proved by experiments that the sensitive post recognition based on this method has high accuracy.And can improve the recognition accuracy with the increase of recognition times. The hot topic recognition and prediction technology this paper focuses on the hot topic recognition and prediction technology.According to the characteristics of campus BBS, a topic recognition method based on the four parameters of topic posting quantity, topic browsing number, topic reply number and topic essence post quantity is put forward in topic recognition.In the prediction of hot topics, the technology of hot topic prediction based on Bayesian network is studied, and the possibility of a topic becoming a hot topic is calculated.The possibility of becoming an ordinary topic and the possibility of becoming a cold topic to predict the trend of public opinion on that topic.These studies can improve the efficiency of campus BBS public opinion monitoring and provide technical support for campus BBS public opinion monitoring.
【學(xué)位授予單位】：福州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.09;TP391.1

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