arXiv subjects

Deepit Sapru

Publications and source records attributed to Deepit Sapru.

4 recordsLinked to original sources

arXiv2026

GeoSense-AI: Fast Location Inference from Crisis Microblogs

This paper presents an applied AI pipeline for real-time geolocation from noisy microblog streams, unifying statistical hashtag segmentation, part-of-speech-driven proper-noun detection, dependency parsing around disaster lexicons, lightweight named-entity recognition, and gazetteer-grounded disambiguation to infer locations directly from text rather than sparse geo-tags. The approach operationalizes information extraction under streaming constraints, emphasizing low-latency NLP components and efficient validation against geographic knowledge bases to support situational awareness during emergencies. In head-to-head comparisons with widely used NER toolkits, the system attains strong F1 while being engineered for orders-of-magnitude faster throughput, enabling deployment in live crisis informatics settings. A production map interface demonstrates end-to-end AI functionality---ingest, inference, and visualization---surfacing locational signals at scale for floods, outbreaks, and other fast-moving events. By prioritizing robustness to informal text and streaming efficiency, GeoSense-AI illustrates how domain-tuned NLP and knowledge grounding can elevate emergency response beyond conventional geo-tag reliance.

cs.CL↗

arXiv2026

Guardrailed Uplift Targeting: A Causal Optimization Playbook for Marketing Strategy

This paper introduces a marketing decision framework that optimizes customer targeting by integrating heterogeneous treatment effect estimation with explicit business guardrails. The objective is to maximize revenue and retention while adhering to constraints such as budget, revenue protection, and customer experience. The framework first estimates Conditional Average Treatment Effects (CATE) using uplift learners, then solves a constrained allocation problem to decide whom to target and which offer to deploy. It supports decisions in retention messaging, event rewards, and spend-threshold assignment. Validated through offline simulations and online A/B tests, the approach consistently outperforms propensity and static baselines, offering a reusable playbook for causal targeting at scale.

cs.LG↗

arXiv2026

Guardrailed Elasticity Pricing: A Churn-Aware Forecasting Playbook for Subscription Strategy

This paper presents a marketing analytics framework that operationalizes subscription pricing as a dynamic, guardrailed decision system, uniting multivariate demand forecasting, segment-level price elasticity, and churn propensity to optimize revenue, margin, and retention. The approach blends seasonal time-series models with tree-based learners, runs Monte Carlo scenario tests to map risk envelopes, and solves a constrained optimization that enforces business guardrails on customer experience, margin floors, and allowable churn. Validated across heterogeneous SaaS portfolios, the method consistently outperforms static tiers and uniform uplifts by reallocating price moves toward segments with higher willingness-to-pay while protecting price-sensitive cohorts. The system is designed for real-time recalibration via modular APIs and includes model explainability for governance and compliance. Managerially, the framework functions as a strategy playbook that clarifies when to shift from flat to dynamic pricing, how to align pricing with CLV and MRR targets, and how to embed ethical guardrails, enabling durable growth without eroding customer trust.

cs.LG↗

arXiv2025

Analyzing Crime Discourse in U.S. Metropolitan Communities on Reddit: Trends, Influences, and Insights

The relationship between crime and the media has long been a focal point of academic research, with traditional media playing a significant role in shaping public perceptions of safety and community well-being. However, the advent of social media has introduced a new dimension to this discourse, offering unique platforms for user-driven discussions. Despite the prominence of social media, research examining its impact on crime-related discourse remains limited. This paper investigates crime-related discussions across Reddit communities representing 384 Metropolitan Areas in the United States. By analyzing user submissions, we identify key trends in crime discourse, including the higher prevalence of such discussions in larger metropolitan areas and communities with more liberal political leanings. Interestingly, we find that reported crime rates do not strongly influence the frequency or intensity of these discussions. These findings provide novel insights into how social media platforms, like Reddit, shape public narratives around crime, highlighting the growing need to examine digital spaces as influential mediators of public perception.

cs.SI↗