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News-Topic Oriented Hashtag Recommendation in Twitter Based on Characteristic Co-occurrence Word Detection

机译：基于特征同现词检测的Twitter中以新闻为主题的主题标签推荐

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Hashtags. which started to be widely used since 2007. are always utilized to mark keywords in tweets to categorize messages and form conversation for topics in Twitter. However, it is hard for users to use hashtags for sharing their opinions/interests/comments for their interesting topics. In this paper, we present a new approach for recommending news-topic oriented hashtags to help Twitter users easily join the conversation about news topics in Twitter. We first detect topic-specific informative words co-occurring with a given target word, which we call characteristic co-occurrence words, from news articles to form a vector for representing the news topic. Then by creating a hashtag vector based on tweets with the same hashtag, we calculate the similarity between these two vectors and recommend hashtags of high similarity scores with the news topic. Experimental results show that our approach could recommend hashtags which are highly relevant to the news topics, helping users share their tweets with others in Twitter.
机译：标签。自2007年以来已开始广泛使用。自始至终始终用于标记推文中的关键字，以对消息进行分类并在Twitter中形成主题的对话。但是，用户很难使用标签来共享他们对有趣主题的观点/兴趣/评论。在本文中，我们提出了一种新方法，用于推荐面向新闻主题的主题标签，以帮助Twitter用户轻松加入Twitter中有关新闻主题的对话。我们首先从新闻文章中检测与给定目标单词同时出现的特定主题的信息性单词，我们称之为特征共现单词，以形成代表新闻主题的向量。然后，通过基于具有相同主题标签的推文创建主题标签向量，我们计算这两个向量之间的相似度，并向新闻主题推荐具有较高相似度得分的主题标签。实验结果表明，我们的方法可以推荐与新闻主题高度相关的主题标签，从而帮助用户在Twitter上与其他人共享其推文。

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