Twitter, a microblogging service, is todays most popular platform for communication in the form of short text messages, called Tweets. Users use Twitter to publish their content either for expressing concerns on information news or views on daily conversations. When this expression emerges, they are experienced by the worldwide distribution network of users and not only by the interlocutor(s). Depending upon the impact of the tweet in the form of the likes, retweets and percentage of followers increases for the user considering a window of time frame, we compute attention factor for each tweet for the selected user profiles. This factor is used to select the top 1000 Tweets, from each user profile, to form a document. Topic modelling is then applied to this document to determine the intent of the user behind the Tweets. After topics are modelled, the similarity is determined between the BBC news data-set containing the modelled topic, and the user document under evaluation. Finally, we determine the top words for a user which would enable us to find the topics which garnered attention and has been posted recently. The experiment is performed using more than 1.1M Tweets from around 500 Twitter profiles spanning Politics, Entertainment, Sports etc. and hundreds of BBC news articles. The results show that our analysis is efficient enough to enable us to find the topics which would act as a suggestion for users to get higher popularity rating for the user in the future.
翻译:Twitter是一种微博客服务,是当今最受欢迎的通信平台,其形式是简短的文本信息,称为 Tweets。用户使用Twitter发布内容,要么表达对信息新闻的关注,要么在日常谈话中表达观点。当这种表达出现时,用户在全世界分发网络中都体验到,而不仅仅是对话者。根据Twitter以类似主题、retweets和追随者增加的百分比等形式对用户的影响,在考虑时间框架窗口的用户中,我们计算每个推文的注意系数。这个系数用于从每个用户简介中选择最顶尖的1000 Tweets,以形成文件。然后对该文件应用主题建模,以确定Tweets背后的用户意图。在进行模拟后,将决定含有模拟主题的BBC新闻数据集与正在评价的用户文件之间的相似性。最后,我们为用户确定最高级的字句,以使我们能够找到获得关注和最近张贴的话题。实验将使用超过11,000 Tweets,从大约500个用户简介中挑选出一个高的Tweets,然后将显示我们的BBCS