Understanding User Perceptions of DeepSeek: A Mixed-Methods Sentiment and Thematic Analysis

Abstract

The research looks at user impressions of large language model (LLM) DeepSeek through a dual analytical framework encompassing sentiment and thematic analysis. The study aims to find user responses' predominant moods and repeated themes. The bifocal approach enhances perceptions of user satisfaction, concerns, and expectations by including sentiments and themes. A mixed-methods approach combining rule-based VADER sentiment analysis and thematic evaluation was used to analyze user feedback to elucidate platform-specific advantages and systemic challenges. Results indicate DeepSeek was perceived as highly positive overall by users (+0.80 aggregate sentiment) as well as high scores for Accessibility (+0.93) and Intelligence & Reasoning (+0.88). Nevertheless, emerging criticism revolved around its Censorship and Content policies (-0.20). The users applauded analytical accuracy through free access but criticized the interface navigation issues. Thematic analysis pinpoints DeepSeek as affluent with analytically driven users while emphasizing its highly tenacious capacity towards domain-specific problems. However, Cross-cutting challenges include latency, stability, and the inherent tension between content moderation and user autonomy. The AI evaluation frameworks are methodologically advanced by adding computational sentiment tools and qualitative perceptions. The intuitions from these findings allow for a better understanding of the user experience and have profound implications for the current development and practical usability of DeepSeek. The relationship between functional specialty and user expectations equates the competitiveness of AI platforms with DeepSeek, seen as a robust, free analytical tool while signaling possible areas for strategic enhancements.

Keywords: LLMs, DeepSeek, AI, user feedback, thematic analysis, sentiment analysis, NLP, VADER