發(fā)布時間：2018-05-27 04:36

  本文選題：情感分類 + 情感傾向　； 參考：《北京郵電大學》2016年碩士論文

【摘要】：近年來,互聯網飛速發(fā)展,社交網站已經成為人們表達觀點的主要平臺。微博作為其中熱門的網站之一,每天都會產生大量的用戶行為數據,這些數據對很多領域都具有研究價值。情感傾向分析是當下熱門的研究領域之一,它使用統計學和機器學習方法對用戶行為數據進行分析和挖掘,并通過分析結果預測用戶的情感態(tài)度。本文主要研究和實現了針對微博文本的情感分析系統,具體內容包括以下六個方面:第一,研究了常用的情感分析算法,包括支持向量機算法、樸素貝葉斯算法、Adaboost算法以及神經網絡算法。研究了四種算法的原理以并對四種算法進行了分析比較。第二,研究了微博平臺頁面布局,設計了分布式微博爬蟲系統。本系統主要爬取微博熱門話題數據,包括微博正文和微博評論。第三,設計了數據預處理系統,并定義了數據預處理的三種規(guī)則,包括表情數據轉化規(guī)則、數據去重規(guī)則以及無效數據清洗規(guī)則。第四,分析了微博文本數據特點,并針對其特點選擇文本特征提取方法。本文主要使用卡方檢驗方法和TF-IDF方法對微博文本提取和表示特征。第五,使用上述分類算法中的前三種構建微博文本分類器,將微博文本分成正向、負向和中性三類,同時對三種算法分類結果進行了比較和分析。第六,設計并實現了一個展示系統,獲取話題數據并通過WEB進行展示。最后,本文基于微博話題數據,對情感分析系統進行了測試,結果表明系統在微博情感預測中表現出較好的效果。
[Abstract]:In recent years, with the rapid development of the Internet, social networking sites have become the main platform for people to express their views. As one of the most popular websites, Weibo produces a lot of user behavior data every day. Affective tendency analysis is one of the most popular research fields. It uses statistics and machine learning methods to analyze and mine user behavior data and predict the emotional attitude of users through the analysis results. This paper mainly studies and implements the emotion analysis system for Weibo text. The specific contents include the following six aspects: first, the commonly used affective analysis algorithms, including support vector machine algorithm, are studied. Naive Bayes algorithm, Adaboost algorithm and neural network algorithm. The principle of four algorithms is studied, and the four algorithms are analyzed and compared. Secondly, the page layout of Weibo platform is studied, and the distributed Weibo crawler system is designed. This system mainly crawls Weibo hot topic data, including Weibo text and Weibo comment. Thirdly, the data preprocessing system is designed, and three rules of data preprocessing are defined, including expression data transformation rule, data de-reduplication rule and invalid data cleaning rule. Fourthly, this paper analyzes the characteristics of Weibo text data, and selects a text feature extraction method according to its characteristics. This paper mainly uses chi-square test method and TF-IDF method to extract and represent Weibo text. Fifthly, Weibo text classifier is constructed by using the first three classification algorithms, and the Weibo text is divided into three categories: forward, negative and neutral. At the same time, the classification results of the three algorithms are compared and analyzed. Sixth, a display system is designed and implemented to obtain topic data and display it through WEB. Finally, based on the topic data of Weibo, this paper tests the affective analysis system, and the results show that the system has a good effect in the prediction of Weibo emotion.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.1;TP393.092

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本文編號：1940469