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摘要: 情感主题联合生成模型已经成功应用于网络评论分析.然而，随着智能终端设备的广泛应用，由于屏幕及输入限制，用户书写的评论越来越短，我们不得不面对短评论中的文本稀疏问题.本文提出了一个针对短文本的联合情感--主题模型SSTM（Short-text sentiment-topic model）来解决稀疏性问题.不同于一般主题模型中通常采用的基于文档产生过程的建模方法，我们直接对整个语料集合的产生过程建模.在产生文档集的过程中，我们每次采样一个词对，同一个词对中的词有相同的情感极性和主题.我们将SSTM模型应用于两个真实网络评论数据集.在三个实验任务中，通过定性分析验证了主题发现的有效性，并与经典方法进行定量对比，SSTM模型的文档级情感分类性能也有较大提升.Abstract: Topic and sentiment joint modelling has been successfully used in sentiment analysis for opinion text. However, we have to face the text sparse problem in opinion text when the length of text becomes shorter and shorter with popularity of smart devices. In this paper, we propose a joint sentiment-topic model SSTM (short-text sentiment-topic model) for short text. Unlike the topic model which models the generative process of each document, we directly model the generation of the whole review set. In the generation process of corpus, we sample a word-pair each time, in which the two words have the same sentiment label and topic. We apply SSTM to two real life social media datasets with three tasks. In the experiment, we demonstrate the effectiveness of the model on topic discovery by qualitative analysis. On the quantitative analysis of document level sentiment classification, SSTM model achieves better performance compared with the existing approaches.
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Key words:
- Sentiment classification /
- sentiment topic model /
- topic model /
- short text topic mode /
- text sparse
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表 1 论文中符号的含义
Table 1 Meanings of the notations
符号 描述 符号 描述 D 文档数量 β φ的非对称Dirichlet先验参数, M 词对数量 β = {{{βz, l, i}k=1T}l=1S}i=1V T 主题数目 α θ的Dirichlet先验参数 S 情感极性数 γ π的Dirichlet先验参数 V 词汇表大小 Θ 主题的多项式分布 b 词对, b = (wi, wj) zt 第t个词的主题 w 词 lt 第t个词的情感极性标签 z 主题 B 词对集合 l 情感极性标签 {z-t} 除第t个词以外的其他所有词的主题分布 πk, l 主题k和情感极性l上的分布 {l-t} 除第t个词以外的其他所有词的情感极性 Π 情感极性标签的多项式分布 Nk, l, i 词wi指派为主题k和情感极性l的次数 φk, l, w 词w基于主题k和情感极性l的分布 Nk, l 指派为主题k和情感极性l的词的数量 Φ 词的多项式分布 N'(·) 句子计数 θk 主题k的分布 Nk 主题k中的词的数量 
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表 2 语料统计信息
Table 2 Statistics of the text corpus
笔记本 手机 文档平均词数 20 32 评论数 3 988 2 289 词汇表大小 7 964 8 787 正面评论数 1 993 1 146 负面评论数 1 995 1 943
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表 3 笔记本数据集中发现的部分主题词列表
Table 3 Example topics discovered from LAPTOP dataset
SSTM BTM LDA 外观 电池 散热性 外观 电池 散热性 外观 电池 散热性 指纹 电池 散热 太 电池 散热 容易 电池 好 钢琴 小时 热 容易 时间 好 指纹 小时 散热 漂亮 长 温度 指纹 小时 不错 外壳 时间 声音 烤漆 比较 好 键盘 键盘 电池 钢琴 长 风扇 好 时间 烫 烤漆 比较 度 烤漆 续航 小 模具 续航 CPU 比较 长 热 表面 比较 温度 屏幕 使用 硬盘 不错 好 温度 亮点 使用 热 外壳 上网 风扇 外壳 不错 声音 感觉 键盘 运行 文字 小 机器 钢琴 使用 使用 说 小巧 轻 呵呵 芯 比较 屏幕 续航 CPU 屏幕 芯 时
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表 4 手机数据集中发现的部分主题词列表
Table 4 Example topics discovered from MOBILE dataset
SSTM BTM LDA 拍照 媒体播放 屏幕 拍照 媒体播放 屏幕 拍照 媒体播放 屏幕 拍摄 播放 屏幕 像素 MP3 屏幕 效果 支持 屏幕 功能 速度 好 摄像头 播放 色 摄像头 MP3 显示 支持 不错 显示 拍摄 耳机 显示 像素 播放 比较 屏幕 影音 色 数码 效果 TFT 拍照 内存 色彩 像素 手机 效果 手机 好 效果 照片 蓝牙 色 材质 处理器 彩色 支持 音乐 色彩 拍摄 卡 清晰 照片 格式 设计 倍 听 手机 拍 格式 高 摄像头 MP3 TFT 效果 功能 好 数码 扩展 铃声 拍照 流畅 机子 相机 不错 26万 相机 文件 方便 数码 文件 人 拍照 比较 像素 倍 视频 TFT
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表 5 笔记本数据集上的CM值(%)
Table 5 CM(%) on laptop dataset
方法 标注员1 标注员2 标注员3 标注员4 平均值 LDA 58 50 60 56 56 BTM 70 66 75 72 70.75 SSTM 69 64 72 67 68
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表 6 手机数据集上的CM值(%)
Table 6 CM(%) on mobile dataset
方法 标注员1 标注员2 标注员3 标注员4 平均值 LDA 69 65 71 74 69.75 BTM 76 74 81 81 78 SSTM 75 72 79 78 76
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表 7 SSTM 发现的部分情感相关的主题词列表
Table 7 Example sentiment-specific topics discovered by SSTM
笔记本 手机 正面 负面 正面 负面 快递 性价比 外观 做工 售后 铃声 外观 按键 输入法 信号 速度 不错 小 有点 电话 铃声 设计 按键 短信 信号 东西 价格 漂亮 禁用 服务 不错 外观 手感 输入法 网络 京东 机器 喜欢 触摸板 差 耳机 不错 感觉 键 差 质量 便宜 买 需要 客服 听 好 好 切换 无 好 款 外观 外壳 送货 声音 感觉 操作 拼音 检测 发货 性能 本本 盖子 快递 放 喜欢 不错 数字 移动 问题 好 不错 小 货 音乐 漂亮 容易 麻烦 关机 比较 电脑 好 老版 无 好 时尚 使用 选 质量 很快 超值 键盘 掉 态度 耳朵 手感 摇杆 手 故障 送货 降价 适合 瑕疵 前台 效果 机身 舒服 标点符号 通话
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表 8 情感极性识别结果(主题数目设置为25)
Table 8 Sentiment identification results (The number of topics is 25.)
基线 JST ASUM SSTM SVM (Uni) SVM (Bi) 笔记本 0.637645 0.50677 0.57754 0.65503 0.66047 0.70021 手机 0.602188 0.53698 0.43694 0.64201 0.64476 0.68953
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