You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: I want to classify reviews on Amazon in English and Chinese.

Keyphrases: text classification

Dataset description:
AmazonQA: AmazonQA consists of 923k questions, 3.6M answers and 14M reviews across 156k products. Building on the well-known Amazon dataset, additional annotations are collected, marking each question as either answerable or unanswerable based on the available reviews.
Chinese Classifier: Classifiers are function words that are used to express quantities in Chinese and are especially difficult for language learners. This dataset of Chinese Classifiers can be used to predict Chinese classifiers from context.
IMDb Movie Reviews: The IMDb Movie Reviews dataset is a binary sentiment analysis dataset consisting of 50,000 reviews from the Internet Movie Database (IMDb) labeled as positive or negative. The dataset contains an even number of positive and negative reviews.
Amazon Fine Foods: Amazon Fine Foods is a dataset that consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012.
SNLI: The SNLI dataset (Stanford Natural Language Inference) consists of 570k sentence-pairs manually labeled as entailment, contradiction, and neutral. Premises are image captions from Flickr30k, while hypotheses were generated by crowd-sourced annotators who were shown a premise and asked to generate entailing, contradicting, and neutral sentences.
OpoSum: OPOSUM is a dataset for the training and evaluation of Opinion Summarization models which contains Amazon reviews from six product domains: Laptop Bags, Bluetooth Headsets, Boots, Keyboards, Televisions, and Vacuums. The six training collections were created by downsampling from the Amazon Product Dataset introduced in McAuley et al.
SST: The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005) and consists of 11,855 single sentences extracted from movie reviews.

Options:- A: AmazonQA  B: Chinese Classifier  C: IMDb Movie Reviews  D: Amazon Fine Foods  E: SNLI  F: OpoSum  G: SST
["SNLI", "SST"]