Exploring Key Issues Affecting African Mobile eCommerce Applications Using Sentiment and Thematic Analysis
Bibliographic record
Abstract
Mobile eCommerce applications are increasingly becoming popular for shopping online in Africa, since the means of accessing the Internet is mostly through mobile devices. This presents an opportunity to explore and understand the key issues affecting African mobile eCommerce applications by performing sentiment analysis of users reviews from seven top African mobile eCommerce applications (i.e., Jiji Nigeria, Jumia, Jiji Kenya, Konga, Takealot, KiliMall and Jiji Uganda). We implement two sentiment analysis approaches, which are Linguistic Inquiry Word Count (LIWC) and Machine Learning (ML), to classify user reviews into positive or negative sentiment polarity. Specifically, we compare five ML algorithms and the LIWC with respect to their performance, and also conduct thematic analysis to uncover the positive and negative factors affecting African mobile eCommerce. Our results show that LIWC is the best performing method with 86.7% F1-score. Our thematic analysis reveals various business, legal, and technology issues, as well as positive factors such as ease of use, fast delivery time, and affordable items. Finally, we offer recommendations on how to tackle the negative issues.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".