Package: sentiment.ai 0.1.1
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.
Authors:
sentiment.ai_0.1.1.tar.gz
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sentiment.ai_0.1.1.tgz(r-4.4-any)sentiment.ai_0.1.1.tgz(r-4.3-any)
sentiment.ai_0.1.1.tar.gz(r-4.5-noble)sentiment.ai_0.1.1.tar.gz(r-4.4-noble)
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sentiment.ai.pdf |sentiment.ai.html✨
sentiment.ai/json (API)
NEWS
| # Install 'sentiment.ai' in R: |
| install.packages('sentiment.ai', repos = c('https://benwiseman.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/benwiseman/sentiment.ai/issues
- airline_tweets - Airline Tweet Data
- default - Default sentiment matching dictionary
Last updated 3 years agofrom:7cb2b5be77. Checks:OK: 3 NOTE: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 31 2024 |
| R-4.5-win | OK | Oct 31 2024 |
| R-4.5-linux | OK | Oct 31 2024 |
| R-4.4-win | NOTE | Oct 31 2024 |
| R-4.4-mac | NOTE | Oct 31 2024 |
| R-4.3-win | NOTE | Oct 31 2024 |
| R-4.3-mac | NOTE | Oct 31 2024 |
Exports:as_py_listcheck_sentiment.aicosinecosine_matchembed_textinit_sentiment.aiinstall_sentiment.aisentiment_matchsentiment_scoresentiment.env
Dependencies:backportsbase64enccliconfigdata.tableglueherejsonlitelatticelifecyclemagrittrMatrixpngprocessxpsR6rappdirsRcppRcppTOMLreticulaterlangroperatorsrprojrootrstudioapitensorflowtfautographtfhubtfrunstidyselectvctrswhiskerwithrxgboostyaml
