There has been a recent line of work to automatically detect the emotions of posts in social media.In literature,studies treat posts independently and detect their emotions separately.Different from previous studies,w...There has been a recent line of work to automatically detect the emotions of posts in social media.In literature,studies treat posts independently and detect their emotions separately.Different from previous studies,we explore the dependence among relevant posts via authors'backgrounds,since the authors with similar backgrounds,e.g.,"gender","location",tend to express similar emotions.However,personal attributes are not easy to obtain in most social media websites.Accordingly,we propose two approaches to determine personal attributes and capture personal attributes between different posts for emotion detection:the Joint Model with Personal Attention Mechanism(JPA)model is used to detect emotion and personal attributes jointly,and capture the attributes-aware words to connect similar people;the Neural Personal Discrimination(NPD)model is employed to determine the personal attributes from posts and connect the relevant posts with similar attributes for emotion detection.Experimental results show the usefulness of personal attributes in emotion detection,and the effectiveness of the proposed JPA and NPD approaches in capturing personal attributes over the state-of-the-art statistic and neural models.展开更多
提出一种机器识别哈萨克语句情感的模型。使用条件随机场CRFs(Conditional Random Fields)对哈萨克语句中的情感关键词进行机器识别,在此基础上结合语句逻辑结构分析,能初步判断出哈萨克语句的喜、怒、哀、俱情感倾向。拓宽了哈萨克语...提出一种机器识别哈萨克语句情感的模型。使用条件随机场CRFs(Conditional Random Fields)对哈萨克语句中的情感关键词进行机器识别,在此基础上结合语句逻辑结构分析,能初步判断出哈萨克语句的喜、怒、哀、俱情感倾向。拓宽了哈萨克语言计算机机处理的范围。展开更多
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.62176174 and 61806137.
文摘There has been a recent line of work to automatically detect the emotions of posts in social media.In literature,studies treat posts independently and detect their emotions separately.Different from previous studies,we explore the dependence among relevant posts via authors'backgrounds,since the authors with similar backgrounds,e.g.,"gender","location",tend to express similar emotions.However,personal attributes are not easy to obtain in most social media websites.Accordingly,we propose two approaches to determine personal attributes and capture personal attributes between different posts for emotion detection:the Joint Model with Personal Attention Mechanism(JPA)model is used to detect emotion and personal attributes jointly,and capture the attributes-aware words to connect similar people;the Neural Personal Discrimination(NPD)model is employed to determine the personal attributes from posts and connect the relevant posts with similar attributes for emotion detection.Experimental results show the usefulness of personal attributes in emotion detection,and the effectiveness of the proposed JPA and NPD approaches in capturing personal attributes over the state-of-the-art statistic and neural models.
