CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor毛文吉
Degree Grantor中国科学院研究生院
Place of Conferral北京
Keyword用户交互意图 言语行为理论 交互意图分析识别
Abstract    近年来,随着社交媒体(微博、Twitter、Facebook等)深入发展和普及,人们越来越依赖于社交媒体分享个人经历、发表观点、表达意愿,并由此产生了海量用户生成内容。其中,交互意图广泛存在于社交媒体的用户讨论中,对社交媒体中用户交互行为的意图进行挖掘和分析可以有效支持舆情监控和辅助决策,在诸多领域具有十分重要的研究意义和应用价值。本论文工作聚焦社交媒体中的用户交互意图挖掘问题,利用智能分析技术手段,研究基于言语行为理论的用户交互意图分类及其识别方法,并以新浪微博数据为例,对所提出的交互意图识别方法进行有效性验证。论文工作包括三个方面:
Other Abstract    With the continuous development and expansion of the social media, there is a growing tendency to share experiences, exchange opinions, and express whishes on social media platform (micro-blog, Twitter, Facebook etc.), causing massive user generated content. These valuable contents, especially users’ online interactions on social affairs and public events, reveal a variety of communicative purposes that implicitly express user intentions. Recognizing intents in users’ online interactive behavior from social media data can effectively identify users’ motives and intents behind communication and provide vital information to aid monitoring, analysis and decision-making in many fields. Focusing on the intention mining problem of online interactive contents in social media, this thesis aims to build a general classification scheme based on the speech act theory and develop various approaches to classify users' utterances towards hot events into different intent categories based on the techniques of social media analytics and intelligence technologies. We evaluate the effectiveness of the proposed approaches using the data from popular social media platform, Sina Weibo. The contents of this thesis include three aspects:
    1. Existing definitions of users' online intents are depend heavily on corpus and domain. To deal with the problem, we build a classification scheme of user intents in online interactions based on the speech act theory, which classifies users' utterances into different intent categories according to their pragmatic functions. On the basis of this, we propose a dictionary-based classification approach to automatically construct performative dictionary using external information sources. Provided the performative dictionary, we can recognize user intents with dictionary. Experimental study using a microblog dataset on public safety events from SinaWeibo shows that our approach can construct a high-quality performative dictionary, which provides effective knowledge for user intent classification and recognition.
    2. To deal with the problem of massive data annotation, we propose an automatic method to label the user intent corpus. In feature-based classification approach, we first analyze the syntax of utterance for contextual compensation, pragmatic enrichment, and redundant filtering. Then, we characterize the semantic, syntactic and platform features considering temporal, subjective and pragmatic factors. Finally, we train feature based classifiers to identify user intents in their online interactions. Experimental study using an auto-labelled microblog dataset on public safety events from SinaWeibo shows that our approach can ease the dependency on corpus and topic, which improves the classification accuracy and effectively identifies the user intents in social media.
    3. To incorporate the context information of dialogue sequence and consider both sentence and paragraph level treatment, we propose an intention recognition approach based on hierarchical model of dialogue sequence. In this approach, we handle online text as a sequence of context utterance and identify multiple intent categories of each online text by sequence learning of HMM model. At the same time, we construct dialogue sequences according to the retweet and reply relations. With a second layer HMM model, we identify intents of dialog sequences and recognize the main intent categories of each online text. Experimental study using a microblog dataset on public safety events from SinaWeibo shows that our approach can effectively identifies user intents in online interactions.
Document Type学位论文
Recommended Citation
GB/T 7714
崔宸熙. 面向社交媒体的用户交互意图分析[D]. 北京. 中国科学院研究生院,2017.
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