Southeast Asia FinTech User Sentiment Dataset

Columns

• review_text: The written content of the user's feedback.
• review_rating: The numerical score assigned by the user, ranging from 1 to 5.
• author_id: A unique identifier associated with the user leaving the review.
• author_name: The display name of the reviewer (e.g., 'A Google user').
• author_app_version: The specific version of the application installed on the user's device at the time of the review.
• review_datetime_utc: The date and time the review was posted, recorded in Coordinated Universal Time.
• review_likes: The number of other users who found the review helpful or liked it.
• application_id: The unique package name identifying the specific application (e.g., ph.com.tala, com.paymaya).
• Index Id: A unique identifier for each record row.

Distribution

• Format: Tabular CSV
• Size: Approximately 197.71 MB
• Volume: 1.11 million records (rows)
• Structure: 9 columns containing text, numerical ratings, timestamps, and identifiers.
• Completeness: 100% valid records for key fields such as ratings and dates, with zero mismatched entries.

Usage

• Sentiment Analysis: Extract trends in public perception towards digital banking and lending services.
• Product Roadmap Planning: Identify specific app versions that caused spikes in negative feedback to guide quality assurance.
• Topic Modelling: Detect recurring themes in user complaints or praise, such as login issues or transaction speed.
• Competitor Benchmarking: Compare rating distributions between legacy banks (e.g., BDO, BPI) and digital-first competitors (e.g., Tala, Tonik).
• Natural Language Processing: Train models on real-world feedback which may include local dialects or code-switching.

Coverage

• Geographic Scope: Philippines (focused on apps serving the PH market).
• Time Range: 19 August 2011 to 08 November 2023.
• Demographic/Sector: Users of financial technology applications including online lending, mobile banking, and e-wallets.
• Data Availability: Includes high-volume reviews for major apps such as Tala (299k+ records) and PayMaya (177k+ records).

License

Who Can Use It

• FinTech Product Managers: To monitor user satisfaction and prioritise feature development.
• Market Researchers: To analyse the adoption and reception of digital finance tools in the Philippines.
• Data Scientists: For building and testing sentiment analysis models.
• Investment Analysts: To gauge brand health and customer loyalty for specific financial institutions.

Dataset Name Suggestions

• Philippine FinTech App Reviews: 12-Year Sentiment Archive
• PH Digital Banking & Lending Feedback Census (2011-2023)
• Manila Mobile Finance: Google Play Store Review Corpus
• Southeast Asia FinTech User Sentiment Dataset

Attributes