metadata
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: ID
dtype: string
- name: DOCUMENT_LEVEL_SCORE
dtype: float64
- name: DOCUMENT_LEVEL_MAGNITUDE
dtype: float64
- name: SENTENCE
dtype: string
- name: SENTENCE_SCORE
dtype: float64
- name: SENTENCE_MAGNITUDE
dtype: float64
- name: LABEL
dtype: int64
- name: LENGTH
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 328225.8010973937
num_examples: 1166
- name: test
num_bytes: 82197.19890260631
num_examples: 292
download_size: 172216
dataset_size: 410423
Dataset Card for "sentiment_data_google"
Dataset for sentiment analysis on sentence level Here we used google API to get doc level and sentiment level Score
- id2label = {0: "NEGATIVE", 1: "POSITIVE",2:"NEUTRAL"}
- label2id = {"NEGATIVE": 0, "POSITIVE": 1,"NEUTRAL":2}