Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (1): 41-46.doi: 10.6040/j.issn.1672-3961.0.2018.341

• Machine Learning & Data Mining • Previous Articles     Next Articles

Features analysis for Chinese irony detection

Rongxiang ZHOU(),Xiuyi JIA*()   

  1. School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing 210094, Jiangsu, China
  • Received:2018-08-13 Online:2019-02-20 Published:2019-03-01
  • Contact: Xiuyi JIA E-mail:zhourongxiang1@163.com;jiaxy@njust.edu.cn
  • Supported by:
    国家自然科学基金(61773208);国家自然科学基金(71671086);国家自然科学基金(61403200)

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Abstract:

The research object was data in microblog. The features of irony detection were studied. In view of the characteristics of microblog and irony detection, a variety of features were constructed, such as emotional phrases, emoticons and so on. The experiments showed that the proposed irony features improved 0.34% on recognition accuracy, 0.74% on recall and 0.18% on F-measure, compared with the existing ones for the imbalanced datasets. The proposed irony features also improved 0.44% on recognition accuracy, 2.54% on recall and 0.14% on F-measure, compared with the existing ones for the balanced datasets.

Key words: irony detection, sentiment analysis, features construction, imbalanced dataset, balanced dataset

CLC Number: 

  • TP391

Table 1

Categories ofdegree adverbs"

类别 程度副词
极量 分外，十分，备加，万分，倍加，异常，尤，尤为，尤其，最，最为，无比，极，极为，极其，极度，极端，格外，殊，深为，特，特别，甚，甚为，绝，绝伦，绝对，绝顶，至，至为，透，透顶，顶，顶顶，非常
高量 大，大为，大大，太，好，好不，好生，很，忒，怪，挺，愈，愈为，愈加，愈发，愈益，更，更为，更其，更加，多，多么，比较，益，益发，相当，较，较为，较比，越，越加，越发，过，过于，过分，颇，颇为，蛮，老
较中量 何其，何等，够，尽，全然，满，还，真
中量 几，几乎，差不多，差点儿
较低量 不大，不太，不很，不甚，不胜
低量 多少，微微，丝毫，些微，有些，有点，有点儿，略，略为，略微，略略，毫，稍，稍为，稍微，稍稍，稍许

Table 2

Models ofemotional phrases"

类型 例子
n+q 凯歌阵阵
q+n 阵阵凯歌
a+(uj)+n 美丽(的)花,最好(的)恭维
b+n 中等身材
n+u 暴风雨般，绅士似的
ad+(uj)+vn 迅速发展，认真(的)咨询
eng+n AA制
d+n 真是棒极了，真是调皮
d+v 互相支援，没法接受，强烈反对
a+(ul)+n 活跃气氛，学好功课，热爱家乡，拉仇恨，支持魅族，抛弃(了)魅族，没牛人
v+a 洗刷干净，喜欢清净
a+(ud)+v 关心(得)不够，油价上升，经济复苏
v+(ud)+d 高兴(得)很
v+r 鼓励他
ad+v 正确领导
v+(ud)+l 吃(得)很饱
v+m 有一套
a+(ud)+d 痛快极了，漂亮(的)很，好(得)很，标致极了
d+a 非常漂亮，比较合适，非常谦虚，比较空，有点特殊，过分良好
a+v 富裕起来
n+(v)+a 态度和蔼，情节较重，外形好，胆子(可)真大
v+(ul)+b 看(了)高兴
m+a 十分壮丽
m+f 100以上
n+f 天亮以后，尖子生里面

Table 3

Precision of three feature systems onunbalanced datasets"

分类器 朴素贝叶斯 逻辑斯蒂回归 支持向量机 决策树 随机森林
一元文法特征 0.445 0.404 0.690 0.642 0.610
文献[10]的反语特征 0.494 0.257 0.728 0.687 0.798
9个反语特征 0.505 0.244 0.729 0.680 0.823

Table 4

Recall of three feature systems on unbalanced datasets"

分类器 朴素贝叶斯 逻辑斯蒂回归 支持向量机 决策树 随机森林
一元文法特征 0.509 0.467 0.393 0.233 0.300
文献[10]的反语特征 0.560 0.513 0.489 0.339 0.321
9个反语特征 0.586 0.521 0.502 0.339 0.311

Table 5

F-measure of three feature systems on unbalanced datasets"

分类器 朴素贝叶斯 逻辑斯蒂回归 支持向量机 决策树 随机森林
一元文法特征 0.472 0.434 0.513 0.348 0.431
文献[10]的反语特征 0.525 0.342 0.585 0.453 0.472
9个反语特征 0.543 0.332 0.595 0.452 0.464

Table 6

Precision of three feature systems on balanced datasets"

分类器 朴素贝叶斯 逻辑斯蒂回归 支持向量机 决策树 随机森林
一元文法特征 0.768 0.699 0.732 0.660 0.739
文献[10]的反语特征 0.803 0.710 0.758 0.691 0.785
9个反语特征 0.803 0.734 0.766 0.698 0.768

Table 7

Recall of three feature systems on balanced datasets"

分类器 朴素贝叶斯 逻辑斯蒂回归 支持向量机 决策树 随机森林
一元文法特征 0.780 0.775 0.873 0.785 0.802
文献[10]的反语特征 0.819 0.779 0.861 0.756 0.870
9个反语特征 0.827 0.808 0.881 0.811 0.885

Table 8

F-measure of three feature systems on balanced datasets"

分类器 朴素贝叶斯 逻辑斯蒂回归 支持向量机 决策树 随机森林
一元文法特征 0.773 0.734 0.795 0.714 0.768
文献[10]的反语特征 0.811 0.742 0.805 0.721 0.824
9个反语特征 0.815 0.768 0.819 0.750 0.821

Fig.1

Precision of three feature systems on differentsize of datasets"

Fig.2

Recall of Three Feature Systems on DifferentSize of Datasets"

Fig.3

F-measure of three feature systems on differentsize of datasets"

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