Exploring Playability and Player Experience in Smartphone Games: A Sentiment and Thematic Analysis of Player Reviews
Bibliographic record
Abstract
The global prevalence and popularity of video gaming continue to rise, with games rapidly advancing in graphics, controls, storylines, and device hardware optimization. Today, video games span a wide range of platforms, including desktops, smartphones, tablets, consoles, virtual reality (VR) headsets, and even smartwatches. Despite platform differences, all games are designed to engage players and foster a positive player experience (PX), driven by diverse motivations. In this digital age, players frequently share feedback through star ratings and descriptive reviews, offering valuable insights into playability and PX. Analyzing this user-generated content is essential for understanding current trends and improving player-centric game design across platforms. This study focuses on smartphone games, motivated by the widespread global use of smartphones. We analyzed 4,595 player reviews from 289 smartphone games across 33 genres on Android and iOS platforms. Our three-step process involved: (1) collecting player reviews, (2) conducting binary sentiment analysis using both machine learning (ML) and lexicon-based approaches, and (3) performing thematic analysis on reviews classified by the best-performing sentiment analysis method. We found that the lexicon-based approach outperformed the machine learning approach, achieving an F1 score of 0.91 compared to the best performing ML model’s F1 score of 0.85. While the thematic analysis approach identified 23 playability and player experience factors grouped into four main categories: in-game advertisements (6 factors), sales and customer service (5 factors), technical issues (9 factors), and game design and gameplay (3 factors). Within the game design and gameplay category, we further identified a total of 34 sub-factors distributed across its three factors i.e., game design (3 sub-factors), gameplay (23 sub-factors), and players’ subjective perspectives (8 sub-factors). This study contributes to the fields of games user research, smartphone games, and player experience through analysis of reasonably large number of player reviews from a significant sample of smartphone games. Further, this work lays the groundwork for more specialized and in-depth research in smartphone gaming domain, thereby deepening our understanding of playability and player experience of smartphone games as well as advancing the notion for a platform-centric game design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".