| # 🇮🇳 Indian Captions Dataset |
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| A large-scale multilingual Instagram caption dataset designed for **Natural Language Processing (NLP), sentiment analysis, text classification, multilingual language modeling, social-media analytics, caption generation, and AI/ML research**. |
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| The dataset contains **105,000+ unique Instagram-style captions** covering multiple languages, writing styles, emotions, sentiments, and social-media categories commonly used in Indian and multilingual social-media contexts. |
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| --- |
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| ## 📊 Dataset Overview |
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| | Property | Details | |
| | ---------------- | -------------------------------------------------- | |
| | Dataset Name | Indian Captions Dataset | |
| | Dataset ID | `devpatel18042004/Indian-captions` | |
| | Total Records | 105,000+ | |
| | Primary Domain | Instagram / Social Media | |
| | Data Type | Text | |
| | Languages | English, Hindi, Gujarati and mixed/romanized forms | |
| | Main Task | NLP / Text Classification / Caption Analysis | |
| | Sentiment Labels | Positive, Neutral, Negative | |
| | Format | Tabular | |
| | License | See License section | |
| | Creator | Dev Patel | |
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| --- |
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| ## 🎯 Purpose |
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| The goal of this dataset is to provide a diverse collection of **Instagram-style captions** that can be used to develop and evaluate NLP and Generative AI systems. |
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| The dataset includes captions representing different: |
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| * 💬 Writing styles |
| * ❤️ Emotions |
| * 😊 Sentiments |
| * 📝 Caption lengths |
| * 🌐 Languages |
| * 🔥 Social-media trends |
| * #️⃣ Hashtag patterns |
| * 🎭 Personality and expression styles |
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| It is particularly useful for research and experimentation involving **Indian-language and code-mixed social-media text**. |
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| --- |
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| ## 🌐 Languages |
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| The dataset contains multilingual and mixed-language captions, including: |
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| * 🇬🇧 English |
| * 🇮🇳 Hindi (Devanagari) |
| * 🇮🇳 Hindi / Hinglish |
| * 🇮🇳 Gujarati |
| * 🇮🇳 Gujarati / Gujlish |
| * 🔀 Mixed-language social-media text |
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| The dataset intentionally includes both native-script and Romanized forms where applicable. |
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| --- |
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| ## 🏷️ Dataset Categories |
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| Captions are organized into different semantic and social-media categories. |
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| Examples include: |
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| * Motivation |
| * Attitude |
| * Romantic |
| * Sad |
| * Happy |
| * Shayari |
| * Friendship |
| * Love |
| * Inspirational |
| * Emotional |
| * Cool |
| * Funny |
| * Lifestyle |
| * Confidence |
| * Festival |
| * Travel |
| * Self-Love |
| * Success |
| * Nature |
| * Celebration |
| * Social Media |
| * and other related caption styles |
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| The exact category distribution may vary across the dataset. |
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| --- |
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| ## 😊 Sentiment |
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| Each caption contains a sentiment label. |
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| Available sentiment classes include: |
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| * `Positive` |
| * `Neutral` |
| * `Negative` |
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| Example: |
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| ```text |
| Caption: |
| "Work hard today and build the life you dream about. 🚀" |
| |
| Sentiment: |
| Positive |
| ``` |
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| --- |
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| ## 📏 Caption Length |
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| Captions are categorized using a length tier. |
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| Possible values include: |
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| * `Short` |
| * `Medium` |
| * `Long` |
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| The dataset also provides numerical measurements: |
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| * `character_count` |
| * `word_count` |
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| These fields make the dataset useful for studying relationships between caption length, sentiment, category, and engagement-oriented writing styles. |
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| ## #️⃣ Hashtags |
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| Many captions contain associated hashtags. |
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| Example: |
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| ```text |
| #motivation #success #goals #mindset #growth |
| ``` |
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| The hashtag field can be used for: |
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| * Hashtag analysis |
| * Topic classification |
| * Social-media trend analysis |
| * Caption recommendation |
| * Hashtag generation |
| * Text-to-hashtag systems |
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| --- |
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| ## 📋 Dataset Schema |
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| Each record contains the following fields: |
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| | Column | Type | Description | |
| | ----------------- | ------- | ------------------------------------- | |
| | `caption_id` | string | Unique identifier for the caption | |
| | `caption_text` | string | Actual caption text | |
| | `language` | string | Language or language variant | |
| | `category` | string | Semantic/social-media category | |
| | `length_tier` | string | Short, Medium, or Long | |
| | `sentiment` | string | Positive, Neutral, or Negative | |
| | `hashtags` | string | Associated hashtags | |
| | `character_count` | integer | Number of characters in the caption | |
| | `word_count` | integer | Number of words in the caption | |
| | `has_emoji` | boolean | Whether the caption contains an emoji | |
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| --- |
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| ## 🧾 Example Records |
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| ```text |
| caption_id: |
| CAP_000001 |
| |
| caption_text: |
| "Rise: Work hard in peace today 🚀" |
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| language: |
| English |
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| category: |
| Motivation |
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| length_tier: |
| Short |
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| sentiment: |
| Positive |
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| hashtags: |
| #ambition #growthmindset #goals #discipline #dreambig |
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| character_count: |
| 32 |
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| word_count: |
| 7 |
| |
| has_emoji: |
| True |
| ``` |
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| Another example: |
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| ```text |
| caption_id: |
| CAP_000003 |
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| caption_text: |
| "Yaad aavi fari — vaat puri. 💧" |
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| language: |
| Gujarati (Gujlish) |
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| category: |
| Sad |
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| length_tier: |
| Short |
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| sentiment: |
| Negative |
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| has_emoji: |
| True |
| ``` |
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| --- |
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| # 🚀 Intended Use |
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| This dataset can be used for a wide range of NLP and Generative AI applications. |
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| ### 1. Sentiment Analysis |
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| Train models to classify social-media captions into: |
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| ```text |
| Positive |
| Neutral |
| Negative |
| ``` |
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| ### 2. Text Classification |
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| Classify captions according to their semantic or social-media category. |
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| Example: |
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| ```text |
| Input: |
| "Never stop believing in yourself." |
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| Output: |
| Motivation |
| ``` |
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| ### 3. Multilingual NLP |
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| The dataset can be used to experiment with multilingual and code-mixed Indian-language NLP. |
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| Potential applications include: |
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| * Hindi NLP |
| * Gujarati NLP |
| * Hinglish NLP |
| * Gujlish NLP |
| * Cross-lingual classification |
| * Language identification |
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| ### 4. Caption Generation |
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| The dataset can be used as training or evaluation data for systems that generate Instagram-style captions. |
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| Example: |
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| ```text |
| Input: |
| Theme = Motivation |
| Language = Gujarati |
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| Output: |
| A motivational Gujarati caption. |
| ``` |
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| ### 5. Caption Recommendation |
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| Build systems that recommend captions based on: |
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| * Mood |
| * Category |
| * Language |
| * Sentiment |
| * Caption length |
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| ### 6. Hashtag Recommendation |
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| Use caption text and category information to develop hashtag recommendation systems. |
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| ### 7. Social Media Analytics |
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| The dataset can support research into: |
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| * Language usage |
| * Sentiment trends |
| * Caption styles |
| * Emoji usage |
| * Hashtag patterns |
| * Multilingual social-media communication |
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| --- |
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| # 🤖 Possible Machine Learning Tasks |
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| The dataset can be adapted for several supervised learning problems. |
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| ### Sentiment Classification |
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| ```text |
| Input: |
| caption_text |
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| Target: |
| sentiment |
| ``` |
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| ### Category Classification |
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| ```text |
| Input: |
| caption_text |
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| Target: |
| category |
| ``` |
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| ### Language Classification |
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| ```text |
| Input: |
| caption_text |
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| Target: |
| language |
| ``` |
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| ### Emoji Prediction |
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| ```text |
| Input: |
| caption_text |
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| Target: |
| has_emoji |
| ``` |
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| ### Caption Length Prediction |
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| ```text |
| Input: |
| caption_text |
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| Target: |
| length_tier |
| ``` |
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| ### Multi-Task Learning |
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| Multiple targets can also be predicted simultaneously: |
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| ```text |
| caption_text |
| ↓ |
| NLP Model |
| ↓ |
| ┌────┼───────────┐ |
| ↓ ↓ ↓ |
| Language Category Sentiment |
| ↓ |
| Emoji |
| ``` |
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| --- |
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| # 🧠 Recommended Models |
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| The dataset can be used with traditional machine-learning algorithms as well as modern Transformer models. |
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| ### Traditional ML |
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| * Logistic Regression |
| * Naive Bayes |
| * Support Vector Machines |
| * Random Forest |
| * XGBoost |
| * LightGBM |
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| ### Transformer Models |
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| Potential models include: |
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| * BERT |
| * RoBERTa |
| * DistilBERT |
| * XLM-RoBERTa |
| * IndicBERT |
| * MuRIL |
| * mBERT |
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| ### Generative Models |
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| The dataset can also be useful for experimentation with: |
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| * Llama |
| * Qwen |
| * Gemma |
| * Mistral |
| * Indic-language LLMs |
| * Other instruction-tuned language models |
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| --- |
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| # 📥 Loading the Dataset |
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| You can load the dataset using the Hugging Face `datasets` library. |
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| ```python |
| from datasets import load_dataset |
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| dataset = load_dataset( |
| "devpatel18042004/Indian-captions" |
| ) |
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| print(dataset) |
| ``` |
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| Access the training split: |
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| ```python |
| train = dataset["train"] |
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| print(train[0]) |
| ``` |
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| # 🐍 Using Pandas |
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| ```python |
| from datasets import load_dataset |
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| dataset = load_dataset( |
| "devpatel18042004/Indian-captions" |
| ) |
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| df = dataset["train"].to_pandas() |
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| print(df.head()) |
| print(df.shape) |
| ``` |
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| --- |
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| # 🔎 Example Filtering |
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| ### Get Gujarati captions |
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| ```python |
| gujarati = df[ |
| df["language"].str.contains( |
| "Gujarati", |
| case=False, |
| na=False |
| ) |
| ] |
| |
| print(gujarati.head()) |
| ``` |
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| ### Get positive captions |
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| ```python |
| positive = df[ |
| df["sentiment"] == "Positive" |
| ] |
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| print(positive.head()) |
| ``` |
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| ### Get motivational captions |
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| ```python |
| motivation = df[ |
| df["category"] == "Motivation" |
| ] |
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| print(motivation.head()) |
| ``` |
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| --- |
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| # 📈 Dataset Statistics |
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| The dataset provides additional metadata for every caption. |
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| These include: |
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| ```text |
| character_count |
| word_count |
| has_emoji |
| length_tier |
| sentiment |
| language |
| category |
| ``` |
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| This makes the dataset suitable for exploratory data analysis and feature engineering. |
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| Possible analyses include: |
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| * Average caption length by language |
| * Sentiment distribution |
| * Category distribution |
| * Emoji usage by category |
| * Hashtag frequency |
| * Language distribution |
| * Short vs. medium vs. long captions |
| * Sentiment by language |
| * Category vs. sentiment |
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| --- |
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| # 🧹 Data Quality |
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| The dataset includes structured metadata intended to make downstream processing easier. |
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| Before using the dataset for production or model training, users should consider performing additional preprocessing such as: |
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| * Duplicate detection |
| * Text normalization |
| * Unicode normalization |
| * Hashtag normalization |
| * Emoji normalization |
| * Language verification |
| * Category validation |
| * Sentiment validation |
| * Train/validation/test splitting |
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| For machine-learning experiments, avoid allowing near-duplicate captions to appear across different splits because this can cause **data leakage** and artificially inflated evaluation scores. |
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| --- |
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| # ⚠️ Limitations |
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| This dataset has several important limitations. |
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| ### Synthetic / Curated Nature |
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| The captions may include generated or curated social-media-style text and should not automatically be interpreted as naturally occurring Instagram posts. |
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| ### Cultural Representation |
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| Although the dataset focuses on Indian and multilingual caption styles, it should **not** be considered a statistically representative sample of all Indian social-media users. |
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| ### Language Classification |
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| Some captions are multilingual, code-mixed, or written using Romanized Indian languages. Therefore, language labels may not always correspond to a single linguistic variety. |
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| ### Sentiment Subjectivity |
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| Sentiment is inherently subjective. Some captions may reasonably be interpreted differently by different annotators or models. |
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| ### Category Overlap |
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| A caption may naturally belong to multiple categories. The provided category should therefore be treated as a dataset label rather than an absolute semantic truth. |
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| --- |
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| # 🔐 Privacy |
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| This dataset is intended to contain caption text and associated metadata rather than personally identifiable user information. |
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| Users of this dataset should not attempt to infer or reconstruct the identity of individuals from caption content. |
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| If this dataset is extended using real-world social-media data, appropriate privacy, consent, platform-policy, and legal requirements should be followed. |
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| # ⚖️ Ethical Considerations |
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| Potential applications should consider: |
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| * Bias in language and sentiment labels |
| * Cultural stereotypes |
| * Misclassification of code-mixed language |
| * Representation imbalance |
| * Automated content generation |
| * Responsible use of social-media text |
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| The dataset should not be used to make high-impact decisions about individuals. |
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| --- |
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| # 📜 License |
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| Please refer to the license specified in the Hugging Face repository. |
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| If you redistribute, modify, or build upon this dataset, follow the applicable license requirements and provide appropriate attribution. |
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| # 👨💻 Dataset Creator |
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| **Dev Patel** |
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| Hugging Face: |
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| `devpatel18042004` |
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| Dataset: |
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| `devpatel18042004/Indian-captions` |
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| # 🤝 Contributions |
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| Contributions and improvements are welcome. |
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| Potential contributions include: |
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| * Additional Indian-language captions |
| * Improved language labels |
| * Better category annotations |
| * Sentiment validation |
| * Duplicate detection |
| * Dataset quality improvements |
| * Additional metadata |
| * Benchmark datasets |
| * Evaluation scripts |
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| # 📌 Citation |
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| If you use this dataset in a research project, application, article, or experiment, please cite the Hugging Face dataset. |
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| ```bibtex |
| @dataset{dev_patel_indian_captions, |
| author = {Dev Patel}, |
| title = {Indian Captions Dataset}, |
| publisher = {Hugging Face}, |
| year = {2026}, |
| url = {https://huggingface.co/datasets/devpatel18042004/Indian-captions} |
| } |
| ``` |
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| --- |
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| # ⭐ Acknowledgement |
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| If this dataset is useful for your project, consider starring the repository on Hugging Face and citing the dataset in your work. |
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| ## 🔗 Dataset |
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| **Hugging Face:** |
| https://huggingface.co/datasets/devpatel18042004/Indian-captions |
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| --- |
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| ## 🏷️ Keywords |
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| `Instagram` `Captions` `Indian Languages` `Hindi` `Gujarati` `Hinglish` `Gujlish` `NLP` `Natural Language Processing` `Sentiment Analysis` `Text Classification` `Multilingual NLP` `Social Media` `Caption Generation` `Hashtag Recommendation` `Generative AI` `Machine Learning` `Deep Learning` `Indian NLP` |
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