本文選題：群體智慧　切入點：社交媒體　出處：《大連理工大學(xué)》2014年碩士論文

【摘要】：社交媒體已逐漸成為人們生活中分享、交流、互動的新平臺,其中,人們根據(jù)不同的興趣、話題等凝聚形成多種群體。在社交群體的討論交流過程中將產(chǎn)生十分龐大的信息量,這些信息具有雜亂、短周期性的特點。因此,從大量信息中獲取高質(zhì)量的信息,為用戶提供更完整的信息和服務(wù)更加受到關(guān)注。社交群體存在并體現(xiàn)了群體智慧,運用群體智慧的思想和技術(shù),分析社交群體的信息,能夠更好的為用戶提供服務(wù)也愈發(fā)重要。 本文主要從群體智慧的角度,針對新浪微博中特定社交群體的文本信息進行研究,分別將群體智慧的思想應(yīng)用到垃圾識別問題和排名算法兩個問題中,為群體中的用戶瀏覽微博提供方便。 (1)垃圾微博隨著社交網(wǎng)絡(luò)的發(fā)展日益增多,本文主要針對新浪話題,結(jié)合群體智慧的思想,從無監(jiān)督學(xué)習(xí)的方法考慮,提出一種基于隨機游走聚類的垃圾微博自動識別模型。該模型針對微博話題,根據(jù)微博間的相似度構(gòu)建微博關(guān)系網(wǎng)絡(luò),通過隨機游走聚類算法對微博進行聚類,從而劃分多個微博群體,最后針對聚類得到的獨立個體或小群體利用垃圾微博的顯性特征識別過濾垃圾微博。實驗證明,該模型可以有效地識別垃圾微博,且效果優(yōu)于傳統(tǒng)的監(jiān)督學(xué)習(xí)方法,尤其是召回率方面表現(xiàn)明顯,可為用戶過濾微博中的垃圾內(nèi)容,提高用戶瀏覽效率。 (2)在對新浪微博的電影微吧群體的研究中,本文主要結(jié)合群體智慧的思想,提出了一種基于蟻群算法的排名模型(ACOR),該模型根據(jù)群體中用戶的偏好以及電影的熱度對電影進行綜合排名。同時,該模型還考慮了微博信息中流露出的情感因素,通過分析和把握用戶對電影的情感傾向,計算其情感值。最后,根據(jù)群體微博計算的情感積累值對熱議的電影進行排名,實現(xiàn)了利用群體微博信息對電影的排名。該排名更加符合用戶的偏好,并且具有一定的實時性,符合微博實時性強的特點,可以有效地為用戶提供電影相關(guān)信息。
[Abstract]:Social media has gradually become a new platform for people to share, communicate, interact with each other in their daily lives, in which people form multiple groups according to their different interests, topics, etc. In the process of discussion and communication among social groups, there will be a very large amount of information. This information is cluttered and short-cyclical. Therefore, getting high quality information from a large amount of information, providing users with more complete information and services, is of greater concern. Social groups exist and embody group wisdom. It is more and more important to analyze the information of social groups by using the ideas and techniques of group intelligence to provide better service to users. From the angle of group intelligence, this paper studies the text information of specific social groups in Sina Weibo, and applies the thought of group intelligence to the problem of garbage recognition and ranking algorithm, respectively. For the group of users to browse Weibo to provide convenience. With the increasing development of social networks, this paper focuses on the topic of Sina, combines the thought of group intelligence, and considers the method of unsupervised learning. This paper presents an automatic identification model of garbage Weibo based on random walk clustering. According to the similarity of Weibo, the model is used to set up a relationship network between Weibo, and to cluster Weibo by random walk clustering algorithm. Finally, according to the dominant characteristics of the garbage Weibo, the model can be used to identify and filter the garbage Weibo. Experimental results show that the model can effectively identify the garbage Weibo. And the effect is better than the traditional supervised learning method, especially the recall rate is obvious, which can filter the spam content of Weibo for users and improve the efficiency of browsing. 2) in the study of the group of Sina Weibo's movie micro-bar, this paper mainly combines the thoughts of group wisdom. In this paper, a ranking model based on ant colony algorithm (ant colony algorithm) is proposed. The model ranks films synthetically according to the preferences of users in the group and the heat of films. At the same time, the model also takes into account the emotional factors revealed in Weibo's information. By analyzing and grasping the emotional tendency of the users to the film, we calculate the emotional value. Finally, according to the emotional accumulation value calculated by Weibo, we rank the hot films. This ranking is more in line with users' preferences, and has the characteristics of real-time and strong real-time, which can effectively provide users with motion-related information.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.092

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本文編號：1694053