Text classification depends on more than choosing a positive or negative label. Tone, context, intent, subject matter, ambiguity, mixed sentiment, domain language, and multi-label cases all influence how content should be categorized. Josisoft builds classification workflows around your label taxonomy, decision rules, edge cases, and QA criteria so unstructured text becomes consistent, model-ready training data.
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Document-, sentence-, and phrase-level sentiment scoring across binary, 3-point, or multi-point Likert scales (very positive to very negative), resolving mixed sentiment, conditional phrasing, and neutral factual statements.
Semantic extraction linking specific aspect terms, features, or product attributes to exact opinion spans and discrete sentiment polarities within complex consumer reviews and user feedback.
Classification of explicit and implicit emotional states across standardized psychological taxonomies (e.g., GoEmotions, Plutchik, Ekman), mapping nuances like frustration, confusion, enthusiasm, and anxiety.
Deep multi-tier taxonomy mapping assigning parent, child, and leaf-level categories to documents, articles, and posts using standardized industry frameworks (e.g., IAB Tech Lab Content Taxonomy, IPTC) or custom enterprise ontologies.
Categorization of incoming customer queries, helpdesk tickets, and CRM messages by operational intent, urgency level, churn risk, and product line to train automated routing and agent-assist models.
Policy-governed multi-label classification isolating toxic content, cyberbullying, targeted harassment, identity-based hate speech, profanity, and offensive language against platform community standards.
Evaluating and scoring digital text content against the Global Alliance for Responsible Media (GARM) framework to protect brand reputation and optimize digital advertising placement.
High-urgency safety annotation identifying credible threats of violence, suicide and self-harm ideation, radicalization, sexual exploitation, and illegal acts for automated platform intervention.
Binary and multi-class classification identifying promotional spam, phishing schemes, financial scams, clickbait headlines, and automated bot-generated repetitive text.
Determining author perspective and ideological position (in favor, against, or neutral) toward specific entities, propositions, or public debates, alongside objective fact versus subjective opinion delineation.
Identification of rhetorical irony, sarcasm, hyperbolic statements, and non-literal expressions where surface-level text contradicts true semantic intent.
Granular qualitative coding of open-ended survey responses, NPS verbatims, and customer testimonials, categorizing underlying drivers behind customer satisfaction, churn, feature requests, and usability complaints.
Identification and tagging of incentivized, machine-generated, or coordinated deceptive customer reviews, inorganic praise, and competitor smear campaigns across e-commerce and marketplace platforms.
Our language teams can work directly inside client-approved text-classification environments supporting single-label, multi-label, sentiment, hierarchy, span-linked attributes, and review workflows, including Label Studio, SuperAnnotate, Kili Technology, or other approved platforms.
Annotators can work inside client-owned classification platforms through approved secure access, following your existing taxonomy, label definitions, hierarchy, keyboard workflow, adjudication rules, and review stages.
When no production annotation platform is available, we can configure controlled project workspaces around your content corpus, label taxonomy, category hierarchy, annotation roles, adjudication rules, and QA stages.
Categorize reviews, support tickets, surveys, conversations, and feedback by sentiment, issue type, product area, urgency, or client-defined service categories.
Classify posts, comments, discussions, reviews, and community content using client-defined topic, quality, relevance, safety, or moderation taxonomies.
Label product feedback by sentiment, feature, defect, delivery issue, support experience, quality concern, or other business-defined attributes.
Categorize documents, messages, reports, tickets, knowledge articles, and internal content according to department, subject, priority, workflow, or custom enterprise taxonomies.
Every annotator, QA reviewer, and project manager signs an NDA before accessing project assets.
Personnel are trained on data confidentiality: strict restrictions on screen sharing, zero tolerance for screen recording or screenshots, and supervised session management.
On-premise operations at our central Durgapur facility enforce controlled local networks, restricted USB and removable media ports, and supervised work environments.
Each client is assigned a dedicated team working in siloed environments, preventing cross-project data contamination and maintaining domain context.
Share a representative text sample, category taxonomy, decision rules, multi-label policy, and QA criteria with our delivery team. We will calibrate the label definitions, annotate a controlled pilot batch, review ambiguous cases and class boundaries, and return the sample for acceptance before production scaling.