What types of social media datasets does SocialIntel offer?

SocialIntel offers datasets from 100+ social media platforms including Reddit, GitHub, YouTube, Medium, Discord, Telegram, and more. Each dataset is enriched with 250+ fields covering sentiment analysis, topic detection, financial signals, engagement metrics, and content classification.

How much does SocialIntel cost?

Catalog datasets up to 10K rows are always free to download. Individual datasets start at $18. Pro plans range from $78 to $504 per month, with annual pricing from $780 to $5,040 per year.

Can I request a custom dataset?

Yes! You can submit a custom dataset request specifying the platform, fields, date range, and format. Custom datasets are priced based on row count, starting at $18.

What formats are datasets available in?

Datasets are available in CSV, JSON, JSONL, and Parquet formats. You can download them directly or access them via our REST API.

Is the data ethically sourced and legal?

Yes. We collect only publicly accessible data from social media platforms using official APIs and permissive robots.txt directives. We comply with each platform's terms of service and applicable laws. See our Legality & Compliance page for a full platform-by-platform breakdown.

A Practical Guide to Cross-Platform Sentiment Analysis

Sentiment analysis varies wildly across platforms. A 4chan post and a LinkedIn article require fundamentally different approaches. Here is our framework for platform-aware sentiment analysis.

Heshan Sanjuka

Founder

Sentiment analysis is one of the most requested enrichment features, but it's also one of the most misapplied. A one-size-fits-all sentiment model trained on product reviews will fail spectacularly on Reddit comments or GitHub issues.

Why Platform Matters

Each platform has its own linguistic norms, content formats, and cultural context:

**Reddit** — Sarcasm-heavy, inside jokes, threaded discussions
**YouTube** — Short, often reactionary comments with emoji emphasis
**GitHub** — Technical language, issue tracking, code references
**Mastodon** — Long-form, conversational, community-specific language
**4chan** — Anonymity-driven, memetic, intentionally ambiguous

Our Platform-Aware Approach

We maintain platform-specific sentiment lexicons and rule sets:

**VADER Customization.** The base VADER lexicon is augmented per platform with platform-specific slang, emoji mappings, and intensity modifiers.

**Context Windows.** Sentiment is calculated not just per-post but within conversational context. A negative reply to a positive parent post carries different weight than standalone negativity.

**Sarcasm Detection.** Our heuristic engine includes sarcasm signals — contrast between positive wording and negative context, hyperbolic language, and platform-specific sarcasm markers.

Validation Results

Cross-platform validation on 50K manually labeled posts:

Overall accuracy: 83% (3-class: positive/neutral/negative)
Platform variance: 78% (4chan) to 89% (Medium)
Improvement over generic VADER: +11 percentage points

Recommendations

— Heshan Sanjuka, Founder

sentiment-analysis nlp cross-platform methodology