一种融合文本语义和情感分析的好友推荐方法

Friend Recommendation Based on Analysis of Users' Emotions and Text Semantics

在现实生活中用户的兴趣和情绪波动很大,而其他的微博特征(如注册信息等)一般变动较少,不能很好地表征用户。提出了一种融合用户文本语义和情感分析的好友推荐方法,根据用户的微博文本内容提取出语义特征,通过语义分析技术来计算特征词的相似度,同时引入了时间因素。在获得语义特征相似的用户之后,又进一步考虑用户的情感特征,根据微博文本中表述用户情感的词汇对用户的情感特点进行分析,进而对上一步产生的结果做优化筛选,得出最终的结果。通过实验表明,加入文本语义和情感分析的好友推荐模型更能够有效地提高推荐的准确度和接受率。

The interests and emotions of users are often varied in their real lives. On the contrary, some other features （such as the profiles） of micro-blog are always unchangeable and they cannot describe the users＇ characteristics very well. Then a novel friend recommendation method merged users＇ text semantics with emotions was proposed. In the model, in order to compute the similarity of friends, some text content features from users＇ micro-blog are extracted and time factor was introduced. Then further consideration on the users＇ emotional characteristics was taken to compute the users＇ similarity through analyzing the emotional words in micro-blog text. Then the final results were gotten. The results of the experiments show that the model can effectively enhance the accuracy and rationality of friend recommendation by adding text semantics and emotions analysis.