2 Best APIs for Moderation in Nigeria

Safe Text Detection API provides automated content moderation and text safety analysis, enabling platforms to screen user-generated text for harmful content including profanity, hate speech, threats, toxic language, spam, and adult content. The API is designed for platforms that allow users to post text — social networks, comment sections, review systems, chat applications, forums, and marketplaces — where manual moderation is impractical at scale. The analysis engine processes input text through multiple classification models simultaneously, returning a confidence score for each category of harmful content detected. The profanity detection model identifies obscene language across multiple languages and writing styles, including creative misspellings and character substitutions commonly used to evade naive keyword filters. Hate speech detection identifies language that targets individuals or groups based on protected characteristics — race, religion, gender, ethnicity, and nationality — with particular attention to context so that academic discussion of sensitive topics is not incorrectly flagged. Toxicity scoring provides a continuous score from 0.0 to 1.0 indicating the overall harmfulness of the text, allowing platform operators to set custom thresholds for different contexts. A comment section on a general-audience news site might apply a stricter threshold than an adults-only debate forum. This configurability makes the API adaptable to diverse platform policies without requiring developers to maintain separate moderation rulesets. Spam detection identifies promotional content, repeated messages, and solicitations that violate community guidelines. For Nigerian online marketplaces and classifieds sites where spam listings are a persistent problem, the spam detection capability helps maintain listing quality and user trust. The multi-language support is critical for Nigerian platforms where users mix English, Yoruba, Hausa, Igbo, and Nigerian Pidgin in their communications — moderation limited to English only misses a significant portion of potentially harmful content. The real-time API response (typically under 100ms) makes it suitable for pre-publication checks — blocking a comment before it posts rather than requiring after-the-fact removal. Nigerian social platforms, community apps, and user-generated content sites that want to maintain a safe environment can integrate Safe Text Detection into their submission pipeline to automatically hold flagged content for human review or reject it outright based on confidence thresholds. The Safe Text Detection API provides a batch processing endpoint that analyzes multiple text strings in a single API call, reducing round-trip latency for applications that need to moderate multiple items simultaneously — such as moderating all comments on a post when it receives a flood of engagement. For Nigerian platforms that experience sudden spikes in user activity around viral content or breaking news events, batch moderation ensures the system scales to handle peak volumes without creating a moderation backlog. Configurable allow-lists let operators specify words or phrases that should not be flagged despite matching moderation patterns — useful for platforms with specific community contexts where certain terms are used differently than in general discourse. A Nigerian medical health platform might allow clinical terminology that a general content moderation rule would flag. The combination of threshold tuning, category selection, and custom allow-lists gives Nigerian platform operators fine-grained control over the moderation behavior without requiring custom model training.

Tisane Text Analysis API is a specialized multilingual natural language processing service focused on deep linguistic analysis, content safety detection, and entity extraction across over 30 languages. Unlike general-purpose NLP APIs that offer surface-level text classification, Tisane performs full grammatical analysis including part-of-speech tagging, dependency parsing, and semantic role labeling, enabling applications that need to understand not just what a text is about but how its meaning is constructed. The abuse and content safety detection capabilities are among the most advanced features. Tisane classifies harmful content into fine-grained categories: personal attack, bigotry, criminal activity, sexual advances, mental abuse, and self-harm — going significantly beyond a binary "toxic/not toxic" classification. Each detected issue is returned with the offending text span, category label, severity score, and offset position in the input string, enabling targeted responses like masking specific words rather than rejecting entire messages. This granularity is valuable for Nigerian social platforms that want nuanced moderation — warning a user about a borderline comment rather than immediately banning them. Entity extraction identifies named entities (people, organizations, locations, dates, products, currencies) with type labels and optional enrichment from linked databases. For Nigerian news platforms, entity extraction can automatically tag articles with relevant people, organizations, and locations, building a structured knowledge graph from unstructured text. This powers features like "see all articles mentioning GTBank" or "all news about Lagos State" without manual tagging by editors. Sentiment analysis returns document-level and aspect-level sentiment — understanding that a restaurant review might express positive sentiment about the food but negative sentiment about the service requires aspect-based analysis that sentence-level or document-level models miss. Nigerian businesses that aggregate customer reviews can use aspect-level sentiment to identify specific product or service attributes that customers love or complain about. Language detection identifies the language of input text, critical for Nigerian platforms where users write in English, Yoruba, Igbo, Hausa, and Nigerian Pidgin. The API handles code-switching (mixing languages within a single sentence), which is common in Nigerian social media communication. Tisane's sentiment analysis returns both document-level and entity-level sentiment scores. At the entity level, the API identifies named entities within the text (people, organizations, products, locations) and returns a sentiment polarity score specifically for how the text refers to each entity. This is a significant technical advantage over document-level sentiment analysis — a product review might express positive sentiment overall while containing negative sentiment toward a specific feature. Nigerian product teams and customer success platforms can use entity-level sentiment to identify specific product attributes drawing negative feedback without manual review. The rule-based approach for many of Tisane's classifiers also makes behavior more predictable and explainable compared to purely neural models, which is important for applications in regulated industries. Tisane's language detection handles texts as short as a single sentence reliably, which is important for social media and messaging contexts where individual short messages need to be identified before routing to the appropriate language-specific moderation model. Nigerian platforms serving multi-lingual communities where short comments might be in any of several languages benefit from accurate short-text language detection.