Dataset: Sentiment Analysis Dataset

About this Dataset

Sentiment Analysis Dataset

This dataset is designed for building and evaluating sentiment and emotion classification models in Natural Language Processing (NLP). It includes two well-known datasets:

GoEmotions: A fine-grained emotion dataset developed by Google, containing 58k English Reddit comments labeled with 27 emotion categories plus Neutral.
DailyDialog: A high-quality multi-turn dialog dataset with emotion and intent annotations, ideal for dialog modeling and conversational AI.

Each dataset is provided in CSV format and includes text samples along with corresponding emotion or sentiment labels.

This dataset is useful for:

Emotion classification and multi-label sentiment analysis.
Fine-tuning transformer models (e.g., BERT, RoBERTa).
Training empathetic conversational agents.
Research in affective computing and human-centered AI.

Tables

Dailydialog

@kaggle.mgmitesh_sentiment_analysis_dataset.dailydialog

656.32 KB
11327 rows
2 columns


CREATE TABLE dailydialog (
  "text" VARCHAR,
  "sentiment" VARCHAR
);

Goemotions 1

@kaggle.mgmitesh_sentiment_analysis_dataset.goemotions_1

5.84 MB
69995 rows
37 columns


CREATE TABLE goemotions_1 (
  "text" VARCHAR,
  "id" VARCHAR,
  "author" VARCHAR,
  "subreddit" VARCHAR,
  "link_id" VARCHAR,
  "parent_id" VARCHAR,
  "created_utc" BIGINT,
  "rater_id" BIGINT,
  "example_very_unclear" BOOLEAN,
  "admiration" BIGINT,
  "amusement" BIGINT,
  "anger" BIGINT,
  "annoyance" BIGINT,
  "approval" BIGINT,
  "caring" BIGINT,
  "confusion" BIGINT,
  "curiosity" BIGINT,
  "desire" BIGINT,
  "disappointment" BIGINT,
  "disapproval" BIGINT,
  "disgust" BIGINT,
  "embarrassment" BIGINT,
  "excitement" BIGINT,
  "fear" BIGINT,
  "gratitude" BIGINT,
  "grief" BIGINT,
  "joy" BIGINT,
  "love" BIGINT,
  "nervousness" BIGINT,
  "optimism" BIGINT,
  "pride" BIGINT,
  "realization" BIGINT,
  "relief" BIGINT,
  "remorse" BIGINT,
  "sadness" BIGINT,
  "surprise" BIGINT,
  "neutral" BIGINT
);

Goemotions 2

@kaggle.mgmitesh_sentiment_analysis_dataset.goemotions_2

5.84 MB
69997 rows
37 columns


CREATE TABLE goemotions_2 (
  "text" VARCHAR,
  "id" VARCHAR,
  "author" VARCHAR,
  "subreddit" VARCHAR,
  "link_id" VARCHAR,
  "parent_id" VARCHAR,
  "created_utc" BIGINT,
  "rater_id" BIGINT,
  "example_very_unclear" BOOLEAN,
  "admiration" BIGINT,
  "amusement" BIGINT,
  "anger" BIGINT,
  "annoyance" BIGINT,
  "approval" BIGINT,
  "caring" BIGINT,
  "confusion" BIGINT,
  "curiosity" BIGINT,
  "desire" BIGINT,
  "disappointment" BIGINT,
  "disapproval" BIGINT,
  "disgust" BIGINT,
  "embarrassment" BIGINT,
  "excitement" BIGINT,
  "fear" BIGINT,
  "gratitude" BIGINT,
  "grief" BIGINT,
  "joy" BIGINT,
  "love" BIGINT,
  "nervousness" BIGINT,
  "optimism" BIGINT,
  "pride" BIGINT,
  "realization" BIGINT,
  "relief" BIGINT,
  "remorse" BIGINT,
  "sadness" BIGINT,
  "surprise" BIGINT,
  "neutral" BIGINT
);

Goemotions 3

@kaggle.mgmitesh_sentiment_analysis_dataset.goemotions_3

5.91 MB
71219 rows
37 columns


CREATE TABLE goemotions_3 (
  "text" VARCHAR,
  "id" VARCHAR,
  "author" VARCHAR,
  "subreddit" VARCHAR,
  "link_id" VARCHAR,
  "parent_id" VARCHAR,
  "created_utc" BIGINT,
  "rater_id" BIGINT,
  "example_very_unclear" BOOLEAN,
  "admiration" BIGINT,
  "amusement" BIGINT,
  "anger" BIGINT,
  "annoyance" BIGINT,
  "approval" BIGINT,
  "caring" BIGINT,
  "confusion" BIGINT,
  "curiosity" BIGINT,
  "desire" BIGINT,
  "disappointment" BIGINT,
  "disapproval" BIGINT,
  "disgust" BIGINT,
  "embarrassment" BIGINT,
  "excitement" BIGINT,
  "fear" BIGINT,
  "gratitude" BIGINT,
  "grief" BIGINT,
  "joy" BIGINT,
  "love" BIGINT,
  "nervousness" BIGINT,
  "optimism" BIGINT,
  "pride" BIGINT,
  "realization" BIGINT,
  "relief" BIGINT,
  "remorse" BIGINT,
  "sadness" BIGINT,
  "surprise" BIGINT,
  "neutral" BIGINT
);