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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (10): 1-11    DOI: 10.11925/infotech.2096-3467.2017.0338
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Analyzing Sentiment Polarity of Comments Based on Attributes
Li Hui, Chai Yaqing()
School of Economics and Management, Xidian University, Xi’an 710126, China
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Abstract  

[Objective] This article tries to quantitatively study the sentiment polarity of online comments base on the targets’ attributes. [Methods] First, we analyzed the comments by their objects, attributes and contents. Then, we extracted the attribute words and the corresponding comment sets. Third, we introduced the attribute factors and calculated their values with the modified TFIDF formula. Finally, we developed a quantitative analysis algorithm based on the attribute features with Python. [Results] Compared to the traditional machine learning classification algorithms (e.g., NB and SVM), our method improved the accuracy of sentiment classification, when the attribute factor was set to equal weight. [Limitations] The comments selection method and the coefficients parameters of the proposed algorithm need to be improved. [Conclusions] Our method could effectively improve the accuracy of the sentiment classification.
Key words： Comment Text      Attribute Factor      Comment Mode      Sentiment Polarity     
Received: 26 April 2017      Published: 08 November 2017
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Hui Li
Yaqing Chai

Cite this article:

Li Hui,Chai Yaqing. Analyzing Sentiment Polarity of Comments Based on Attributes. Data Analysis and Knowledge Discovery, 2017, 1(10): 1-11.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.0338     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I10/1

第一个词 第二个词 第三个词
模式1 JJ NN, NNS anything
模式2 RB, RBR, or RBS JJ not NN or NNS
模式3 NN or NNS JJ not NN or NNS
模式4 JJ JJ not NN or NNS
模式5 RB, RBR, or RBS VB, VBD, VBN,
or VBG		 anything
类型 连词
转折 但是、偏偏、只是、不过、至于、不料、岂知、虽然、然而、而、即使、但、可是、不过、却
递进 而且、更、更加、并、甚至、不如、不及、乃至、并且、况、况且、何况
数据集 标注好的正面
评论数目		 标注好的负面
评论数目
chnSenticorp2000 1 000 1 000
chnSenticorp4000 2 000 2 000
chnSenticorp6000 3 000 3 000
chnSenticorp10000 3 000 7 000
属性(Feature) 属性词
F1: 环境 风景、环境、氛围、外观、外表、条件、卫生、空气、酒店环境、酒店氛围、宾馆、周围、周围环境、周边环境、大堂、大堂环境、外观、门面、室内环境、室内、屋内、房子、房间、楼道、走廊、气味、味道、霉味、油漆味、烟味、噪音、噪声
F2: 设施 设施、设计、风格、配套、设备、设置、布置、装置、配备、装备、内饰、内里、建筑、格局、硬件、硬件设施、软件、软件设施、装修、卧具、家具、电梯、客房、标准间、房间面积、房间大小、光线、空间、电视、网络、网速、上网、宽带、空调、墙壁、墙纸、床、毛巾、床单、被罩、被褥、地毯、地板、地面、卫生间、洗手间、厕所、浴室、淋浴、浴缸、热水、洗澡、洗漱用品、个人用品、房间隔音、隔音、停车场、停车、周围设施、通风
F3: 餐饮 餐饮、就餐、餐厅、饭菜、上菜、点餐、叫餐、早餐、早茶、早点、早饭、自助餐、下午茶、饮食、味道、品种、种类、吃饭
F4: 交通 交通、周围交通、路线、出行、外出、打车、进出、购物、景点
F5: 服务 服务态度、态度、表情、语气、口气、服务意识、服务员态度、服务、服务水平、素质、服务素质、前台、服务员、门童、服务生、前台服务、酒店服务、管理、退房、客服
F6: 价格 价格、收费、价钱、价位、性价比、房价、结账、账单、手续
F7: 位置 地理位置、位置、地位、地点、地方、地段、场所、火车站、机场
情感词典 积极词汇 消极词汇 总数
HowNet 4 566 4 370 8 851
NTUSD 2 846 8 325 10 027
Correct label
True False
Positive TP(True Positive) FP(False Positive)
Negative TN(True Negative) FN(False Negative)
属性 环境 设施 餐饮 交通 服务 价格 位置
属性因子 0.501406 0.042195 0.029424 0.005845 0.389272 0.019860 0.011962
属性因子(对照) 0.142857 0.142857 0.142857 0.142857 0.142857 0.142857 0.142857
评论序列 预处理后的评论 提取属性情感对 POS标注 计算情感极值 情感分类 备注
Comment1 |风景还算不错|酒店早餐很难吃 <风景, 不错, 还算>
<早餐, 难吃, 很>		 无 -0.305203935 N 1表示无
程度副词
Comment2 |房间家具太差|早餐质量太差|环境好但交通太差 <家具, 大, 1>
<早餐, 差, 太>
<环境, 好, 1>
<交通, 差, 太>		 但: 转折连词 -1.532515171 N 无
Comment3 |但房间里的淋浴设施不好|前台小姐服务很不好|服务意识太差 <设施, 不好, 1>
<服务, 不好, 很>
<服务意思, 差, 太>		 无 -2.035849256 N 无
Comment4 |环境比较温馨|房间比较干净|卫生间设施较完备 <环境, 温馨, 比较>
<房间, 干净, 比较>
<设施, 完善, 较>		 无 0.709084635 P 无
Comment5 |虽然房间的条件略显简陋|但环境、服务还有饭菜都还是很不错的 <条件, 简陋, 1>
<环境, 不错, 很>
<服务, 不错, 很>
<饭菜, 不错, 很>		 但: 转者连词 0.405485228 P 无
语料库 Accuracy
属性因子
等权重		 传统分类方法 本文
算法
NB SVM
chnSenticorp2000 88.33% 0.791 0.879 89.23%
chnSenticorp4000 89.56% 0.832 0.881 89.90%
chnSenticorp6000 90.01% 0.854 0.908 91.45%
chnSenticorp10000 91.59% 0.873 0.911 92.88%
语料库 F1
属性因子
等权重		 传统分类方法 本文
算法
NB SVM
chnSenticorp2000 80.32% 0.732 0.793 81.13%
chnSenticorp4000 80.57% 0.792 0.801 82.60%
chnSenticorp6000 82.31% 0.801 0.818 84.25%
chnSenticorp10000 82.69% 0.809 0.821 85.19%
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