Purpose-The purpose of the paper is to study multiple viewpoints which are required to access the more informative similarity features among the tweets documents,which is useful for achieving the robust tweets data cl...Purpose-The purpose of the paper is to study multiple viewpoints which are required to access the more informative similarity features among the tweets documents,which is useful for achieving the robust tweets data clustering results.Design/methodology/approach-Let“N”be the number of tweets documents for the topics extraction.Unwanted texts,punctuations and other symbols are removed,tokenization and stemming operations are performed in the initial tweets pre-processing step.Bag-of-features are determined for the tweets;later tweets are modelled with the obtained bag-of-features during the process of topics extraction.Approximation of topics features are extracted for every tweet document.These set of topics features of N documents are treated as multi-viewpoints.The key idea of the proposed work is to use multi-viewpoints in the similarity features computation.The following figure illustrates multi-viewpoints based cosine similarity computation of the five tweets documents(here N 55)and corresponding documents are defined in projected space with five viewpoints,say,v_(1),v_(2),v_(3),v4,and v5.For example,similarity features between two documents(viewpoints v_(1),and v_(2))are computed concerning the other three multi-viewpoints(v_(3),v4,and v5),unlike a single viewpoint in traditional cosine metric.Findings-Healthcare problems with tweets data.Topic models play a crucial role in the classification of health-related tweets with finding topics(or health clusters)instead of finding term frequency and inverse document frequency(TF-IDF)for unlabelled tweets.Originality/value-Topic models play a crucial role in the classification of health-related tweets with finding topics(or health clusters)instead of finding TF-IDF for unlabelled tweets.展开更多
文摘目的探讨完全电视胸腔镜手术(c VATS)治疗早期非小细胞肺癌(NSCLC)患者的临床效果。方法研究对象选取我院收治的114例NSCLC患者,观察组采用c VATS(58例),对照组采用开胸肺叶切除术(56例),比较两组患者的手术效果。结果观察组患者术中出血量、切口长度明显小于对照组,术后置管时间、住院天数、止痛药物使用时间均短于对照组,术后疼痛评分及并发症发生率均低于对照组(P<0.05)。结论 c VATS能促进患者术后恢复,减少并发症发生。
文摘Purpose-The purpose of the paper is to study multiple viewpoints which are required to access the more informative similarity features among the tweets documents,which is useful for achieving the robust tweets data clustering results.Design/methodology/approach-Let“N”be the number of tweets documents for the topics extraction.Unwanted texts,punctuations and other symbols are removed,tokenization and stemming operations are performed in the initial tweets pre-processing step.Bag-of-features are determined for the tweets;later tweets are modelled with the obtained bag-of-features during the process of topics extraction.Approximation of topics features are extracted for every tweet document.These set of topics features of N documents are treated as multi-viewpoints.The key idea of the proposed work is to use multi-viewpoints in the similarity features computation.The following figure illustrates multi-viewpoints based cosine similarity computation of the five tweets documents(here N 55)and corresponding documents are defined in projected space with five viewpoints,say,v_(1),v_(2),v_(3),v4,and v5.For example,similarity features between two documents(viewpoints v_(1),and v_(2))are computed concerning the other three multi-viewpoints(v_(3),v4,and v5),unlike a single viewpoint in traditional cosine metric.Findings-Healthcare problems with tweets data.Topic models play a crucial role in the classification of health-related tweets with finding topics(or health clusters)instead of finding term frequency and inverse document frequency(TF-IDF)for unlabelled tweets.Originality/value-Topic models play a crucial role in the classification of health-related tweets with finding topics(or health clusters)instead of finding TF-IDF for unlabelled tweets.
