微博情感分析是社会媒体挖掘中的重要任务之一,在恐怖组织识别、个性化推荐、舆情分析等方面具有重要的理论和应用价值.但与传统文本数据不同,微博消息短小而凌乱,包含着大量诸如微博表情符号之类的特有信息,同时微博情感是与其讨论主...微博情感分析是社会媒体挖掘中的重要任务之一,在恐怖组织识别、个性化推荐、舆情分析等方面具有重要的理论和应用价值.但与传统文本数据不同,微博消息短小而凌乱,包含着大量诸如微博表情符号之类的特有信息,同时微博情感是与其讨论主题是密切相关的.多数现有的微博情感分析方法都没有将微博主题与微博情感进行协同分析,或者在微博主题情感分析过程中没有考虑将用户关系、用户性格情绪等特征数据,从而导致微博情感分析与主题检测的效果难尽人意.为此,提出了一个基于多特征融合的微博主题情感挖掘模型TSMMF(Topic Sentiment Model based on Multi-feature Fusion),该模型将情感表情符号与微博用户性格情绪特征纳入到图模型LDA中实现微博主题与情感的同步推导.实验结果表明,与当前用于短文本情感主题挖掘的最优模型(JST,SLDA与DPLDA)相比较,TSMMF具有更优的微博主题情感检测性能.展开更多
Microblogs currently play an important role in social communication. Hot topics currently being tweeted can quickly become popular within a very short time as a result of retweeting. Gaining an understanding of the re...Microblogs currently play an important role in social communication. Hot topics currently being tweeted can quickly become popular within a very short time as a result of retweeting. Gaining an understanding of the retweeting behavior is desirable for a number of tasks such as topic detection, personalized message recommendation, and fake information monitoring and prevention. Interestingly, the propagation of tweets bears some similarity to the spread of infectious diseases. We present a method to model the tweets' spread behavior in microblogs based on the classic Susceptible-Infectious-Susceptible (SIS) epidemic model that was developed in the medical field for the spread of infectious diseases. On the basis of this model, future retweeting trends can be predicted. Our experiments on data obtained from the Chinese micro-blogging website Sina Weibo show that the proposed model has lower predictive error compared to the four commonly used prediction methods.展开更多
Topic Detection in News Video and Audio is to automatically detect snippets with a topic the user searches for, in the news streams, including video,audio and broadcasting. It is a novel research scope rises along wit...Topic Detection in News Video and Audio is to automatically detect snippets with a topic the user searches for, in the news streams, including video,audio and broadcasting. It is a novel research scope rises along with the rapid development of multimedia technology, automatic speech recognition and natural language processing. This technology detects the topic of the news in the semantic level and fits for most people's retrieval need.展开更多
文摘微博情感分析是社会媒体挖掘中的重要任务之一,在恐怖组织识别、个性化推荐、舆情分析等方面具有重要的理论和应用价值.但与传统文本数据不同,微博消息短小而凌乱,包含着大量诸如微博表情符号之类的特有信息,同时微博情感是与其讨论主题是密切相关的.多数现有的微博情感分析方法都没有将微博主题与微博情感进行协同分析,或者在微博主题情感分析过程中没有考虑将用户关系、用户性格情绪等特征数据,从而导致微博情感分析与主题检测的效果难尽人意.为此,提出了一个基于多特征融合的微博主题情感挖掘模型TSMMF(Topic Sentiment Model based on Multi-feature Fusion),该模型将情感表情符号与微博用户性格情绪特征纳入到图模型LDA中实现微博主题与情感的同步推导.实验结果表明,与当前用于短文本情感主题挖掘的最优模型(JST,SLDA与DPLDA)相比较,TSMMF具有更优的微博主题情感检测性能.
基金supported by National Natural Science Foundation of China under Grants No. 60773156, No. 61073004Chinese Major State Basic Research Development 973 Program under Grant No. 2011CB302203-2Important National Science &Technology Specific Program under Grant No. 2011ZX01042001-002-2
文摘Microblogs currently play an important role in social communication. Hot topics currently being tweeted can quickly become popular within a very short time as a result of retweeting. Gaining an understanding of the retweeting behavior is desirable for a number of tasks such as topic detection, personalized message recommendation, and fake information monitoring and prevention. Interestingly, the propagation of tweets bears some similarity to the spread of infectious diseases. We present a method to model the tweets' spread behavior in microblogs based on the classic Susceptible-Infectious-Susceptible (SIS) epidemic model that was developed in the medical field for the spread of infectious diseases. On the basis of this model, future retweeting trends can be predicted. Our experiments on data obtained from the Chinese micro-blogging website Sina Weibo show that the proposed model has lower predictive error compared to the four commonly used prediction methods.
文摘Topic Detection in News Video and Audio is to automatically detect snippets with a topic the user searches for, in the news streams, including video,audio and broadcasting. It is a novel research scope rises along with the rapid development of multimedia technology, automatic speech recognition and natural language processing. This technology detects the topic of the news in the semantic level and fits for most people's retrieval need.
