Movie reviews (comment + rating) for 10 movies gathered from IMDB website

We have created manually 10 datasets for 10 different movies, each one contains 100 reviews (comment + rating) randomly extracted and we made sure that the datasets are representatives based on IMDB users weighted average vote.
For each movie, we have created 100 files named from 1 to 100, each one of them contains an opinion toward the target movie. expressed in natural language (ENG). Furthermore, we have created a file named "rating.txt" that contains an arrray of numbers, each number repsresents the rating (numeric scale) attached to a specific opinion toward the target movie.
This is what the rating file looks like:
rating = [ 9 , 10 , 3 ,...................]
Interpretation :
The user that posted the review stored in "1.txt" file has given a 9/10 as rating to the target movie.
The user that posted the review stored in "2.txt" file has given a 10/10 as rating to the target movie.
The user that posted the review stored in "3.txt" file has given a 3/10 as rating to the target movie.

我们针对10部不同的电影手动构建了10个数据集，每个数据集均包含随机抽取的100条影评（含评论内容与评分），且我们基于IMDB用户加权平均评分确保了数据集的代表性。针对每部电影，我们创建了100个命名为1至100的文件，每个文件均包含一段以自然语言（英语）撰写的、针对该目标电影的评价。此外，我们还创建了一个名为"rating.txt"的文件，其中包含一组数值，每个数值分别对应一条针对目标电影的特定评价所附带的评分（数值标度）。该评分文件的格式示例如下：
`rating = [ 9 , 10 , 3 , ...................]`
释义如下：
存储于"1.txt"中的影评对应的用户为该目标电影打出了9/10的评分。
存储于"2.txt"中的影评对应的用户为该目标电影打出了10/10的评分。
存储于"3.txt"中的影评对应的用户为该目标电影打出了3/10的评分。