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+sentiment_analysis_testa.csv filter=lfs diff=lfs merge=lfs -text
+sentiment_analysis_trainingset.csv filter=lfs diff=lfs merge=lfs -text
+sentiment_analysis_validationset.csv filter=lfs diff=lfs merge=lfs -text

README.md

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----
-license: mit
----
+# 说明
+
+数据集来源于[AI Challenger 2018](https://github.com/AIChallenger/AI_Challenger_2018)
+
+sentiment_analysis_trainingset.csv 为训练集数据文件，共105000条评论数据
+sentiment_analysis_validationset.csv 为验证集数据文件，共15000条评论数据
+sentiment_analysis_testa.csv 为测试集A数据文件，共15000条评论数据
+
+数据集分为训练、验证、测试A与测试B四部分。数据集中的评价对象按照粒度不同划分为两个层次，层次一为粗粒度的评价对象，例如评论文本中涉及的服务、位置等要素；层次二为细粒度的情感对象，例如“服务”属性中的“服务人员态度”、“排队等候时间”等细粒度要素。评价对象的具体划分如下表所示。
+
+The dataset is divided into four parts: training, validation, test A and test B. This dataset builds a two-layer labeling system according to the evaluation granularity:  the first layer is the coarse-grained evaluation object, such as “service” and “location”; the second layer is the fine-grained emotion object, such as “waiter’s attitude” and “wait time” in “service” category. The specific description is shown in the following table.
+
+|层次一(The first layer)|层次二(The second layer)|
+|---|---|
+|位置(location)|交通是否便利(traffic convenience)|
+|-|距离商圈远近(distance from business district)|
+|-|是否容易寻找(easy to find)|
+|服务(service)|排队等候时间(wait time)|
+|-|服务人员态度(waiter’s attitude)|
+|-|是否容易停车(parking convenience)|
+|-|点菜/上菜速度(serving speed)|
+|价格(price)|价格水平(price level)|
+|-|性价比(cost-effective)|
+|-|折扣力度(discount)|
+|环境(environment)|装修情况(decoration)|
+|-|嘈杂情况(noise)|
+|-|就餐空间(space)|
+|-|卫生情况(cleaness)|
+|菜品(dish)|分量(portion)|
+|-|口感(taste)|
+|-|外观(look)|
+|-|推荐程度(recommendation)|
+|其他(others)|本次消费感受(overall experience)|
+|-|再次消费的意愿(willing to consume again)|
+
+每个细粒度要素的情感倾向有四种状态：正向、中性、负向、未提及。使用[1,0,-1,-2]四个值对情感倾向进行描述，情感倾向值及其含义对照表如下所示：
+
+There are four sentimental types for every fine-grained element: Positive, Neutral, Negative and Not mentioned, which are labelled as 1, 0, -1 and-2. The meaning of these four labels are listed below.
+
+|情感倾向值(Sentimental labels)|含义(Meaning)|
+|---|---|
+|1|正面情感(Positive)
+|0|中性情感(Neutral)
+|-1|负面情感（Negative）
+|-2|情感倾向未提及（Not mentioned）
+
+数据标注示例如下：
+An example of one labelled review:
+>味道不错的面馆，性价比也相当之高，分量很足~女生吃小份，胃口小的，可能吃不完呢,。环境在面馆来说算是好的，至少看上去堂子很亮，也比较干净，一般苍蝇馆子还是比不上这个卫生状况的。中午饭点的时候，人很多，人行道上也是要坐满的，隔壁的冒菜馆子，据说是一家，有时候也会开放出来坐吃面的人。
+
+|层次一(The first layer)|层次二(The second layer)|标注 (Label)|
+|---|---|---|
+|位置(location)|交通是否便利(traffic convenience)|-2
+|-|距离商圈远近(distance from business district)|-2
+|-|是否容易寻找(easy to find)|-2
+|服务(service)|排队等候时间(wait time)|-2
+|-|服务人员态度(waiter’s attitude)|-2
+|-|是否容易停车(parking convenience)|-2
+|-|点菜/上菜速度(serving speed)|-2
+|价格(price)|价格水平(price level)|-2
+|-|性价比(cost-effective)|1
+|-|折扣力度(discount)|-2
+|环境(environment)|装修情况(decoration)|1
+|-|嘈杂情况(noise)|-2
+|-|就餐空间(space)|-2
+|-|卫生情况(cleaness)|1
+|菜品(dish)|分量(portion)|1
+|-|口感(taste)|1
+|-|外观(look)|-2
+|-|推荐程度(recommendation)|-2
+|其他(others)|本次消费感受(overall experience)|1
+|-|再次消费的意愿(willing to consume again)|-2

sentiment_analysis_testa.csv

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sentiment_analysis_trainingset.csv

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sentiment_analysis_validationset.csv

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