sentiment.ai
Simple Sentiment Analysis Using Deep Learning
Sentiment Analysis via deep learning and gradient boosting models with a lot of the underlying hassle taken care of to make the process as simple as possible. In addition to out-performing traditional, lexicon-based sentiment analysis (see <https://benwiseman.github.io/sentiment.ai/#Benchmarks>), it also allows the user to create embedding vectors for text which can be used in other analyses. GPU acceleration is supported on Windows and Linux.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | sentiment.ai_0.1.1.tar.gz |
863.8 KiB |
0.1.1 |
rolling linux/noble R-4.5 | sentiment.ai_0.1.1.tar.gz |
863.9 KiB |
0.1.1 |
rolling source/ R- | sentiment.ai_0.1.1.tar.gz |
659.7 KiB |
0.1.1 |
latest linux/jammy R-4.5 | sentiment.ai_0.1.1.tar.gz |
863.8 KiB |
0.1.1 |
latest linux/noble R-4.5 | sentiment.ai_0.1.1.tar.gz |
863.9 KiB |
0.1.1 |
latest source/ R- | sentiment.ai_0.1.1.tar.gz |
659.7 KiB |
0.1.1 |
2026-04-26 source/ R- | sentiment.ai_0.1.1.tar.gz |
659.7 KiB |
0.1.1 |
2026-04-23 source/ R- | sentiment.ai_0.1.1.tar.gz |
659.7 KiB |
0.1.1 |
2026-04-09 windows/windows R-4.5 | sentiment.ai_0.1.1.zip |
866.9 KiB |
0.1.1 |
2025-04-20 source/ R- | sentiment.ai_0.1.1.tar.gz |
659.7 KiB |
Dependencies (latest)
Imports
- data.table (>= 1.12.8)
- jsonlite
- reticulate (>= 1.16)
- roperators (>= 1.2.0)
- stats
- tensorflow (>= 2.2.0)
- tfhub (>= 0.8.0)
- utils
- xgboost