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https://github.com/minimaxir/reactionrnn
Python module + R package to predict the reactions to a given text using a pretrained recurrent neural network.
https://github.com/minimaxir/reactionrnn
deep-learning keras r sentiment-analysis tensorflow
Last synced: 24 days ago
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Python module + R package to predict the reactions to a given text using a pretrained recurrent neural network.
- Host: GitHub
- URL: https://github.com/minimaxir/reactionrnn
- Owner: minimaxir
- License: mit
- Created: 2017-08-21T13:24:00.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2019-02-10T15:25:53.000Z (over 5 years ago)
- Last Synced: 2024-09-30T10:02:42.541Z (about 1 month ago)
- Topics: deep-learning, keras, r, sentiment-analysis, tensorflow
- Language: Python
- Homepage:
- Size: 1.15 MB
- Stars: 299
- Watchers: 18
- Forks: 33
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# reactionrnn
reactionrnn is a Python 2/3 module + R package on top of [Keras](https://github.com/fchollet/keras)/[TensorFlow](https://www.tensorflow.org) which can easily predict the proportionate reactions (love, wow, haha, sad, angry) to a given text using a pretrained recurrent neural network.
```python
from reactionrnn import reactionrnnreact = reactionrnn()
react.predict("Happy Mother's Day from the Chicago Cubs!")
```
```
[('love', 0.9765), ('wow', 0.0235), ('haha', 0.0), ('sad', 0.0), ('angry', 0.0)]
```Unlike traditional sentiment analysis models using tools like [word2vec](https://en.wikipedia.org/wiki/Word2vec)/[doc2vec](https://radimrehurek.com/gensim/models/doc2vec.html), reactionrnn handles text at the character level, allowing it to incorporate capitalization, grammar, text length, and sarcasm in its predictions.
```
> react.predict("This is scary AF!😱😱")
[('wow', 0.9109), ('sad', 0.0891), ('love', 0.0), ('haha', 0.0), ('angry', 0.0)]
``````
> react.predict("When the soup is too hot 😂😂😂")
[('haha', 0.8568), ('love', 0.1376), ('wow', 0.0056), ('sad', 0.0), ('angry', 0.0)]
``````
> react.predict("He was only 41.")
[('sad', 1.0), ('love', 0.0), ('wow', 0.0), ('haha', 0.0), ('angry', 0.0)]
``````
> react.predict("Everyone loves autoplaying videos!")
[('angry', 0.8667), ('wow', 0.1333), ('love', 0.0), ('haha', 0.0), ('sad', 0.0)]
```As a bonus, the model can encode text as a 256D vector (incorporating grammar/caps/length/punc) which can then be fed into other machine learning/deep learning models.
```
> react.encode("DYING. 😄")
[ 0.0411452 0.87985831 0.31406021, ...]
```Did I mention that reactionrnn is also available as an R package with feature parity?
```
library(reactionrnn)
react <- reactionrnn()
react %>% predict("Happy Mother's Day from the Chicago Cubs!")
``````
love wow haha sad angry
0.97649449 0.02350551 0.00000000 0.00000000 0.00000000
```## Usage
For Python, reactionrnn can be installed [from pypi](https://pypi.python.org/pypi/reactionrnn) via `pip`:
```
python3 -m pip install reactionrnn
```You may need to create a venv (`python3 -m venv `) first.
For R, you can install reactionrnn from this GitHub repo with devtools (working on resolving issues to get package on CRAN):
```
# install.packages('devtools')
devtools::install_github("minimaxir/reactionrnn", subdir="R-package")
```You can view a demo of common features in [this Jupyter Notebook](/docs/reactionrnn-demo-python.ipynb) for Python, and [this R Notebook](http://minimaxir.com/notebooks/reactionrnn/) for R. (full documentation coming soon)
## Neural Network Architecture and Implementation
![](/docs/model_shapes.png)
reactionrnn is based off of the June 2016 blog post I wrote titled [Classifying the Emotions of Facebook Posts Using Reactions Data](http://minimaxir.com/2016/06/interactive-reactions/), which noted that there is a certain nuance to the proportionality of the reactions on a Facebook status. What makes a Facebook post "WOW" but *not* "HAHA"? Is there a semantic difference between a post with 75% SAD and 90% SAD? A year later, Facebook now has enough public data to sufficiently train a neural network to understand these nuances.
reactionrnn takes in an input of up to 140 characters (for compatability with Twitter tweets), converts each character to a 100D character embedding vector, and feeds those into a 256-cell [gated recurrent unit](https://en.wikipedia.org/wiki/Gated_recurrent_unit) layer. That output regresses the five non-Like Reactions all simultaneously and outputs the predicted proportionality values for each; predicted values will always sum to 1 (unlike Google's [Perspective API](https://www.perspectiveapi.com), the output is **not** the probability of the label as is the case with a classification model!)
The 1.3MB model weights included with the package are trained on the captions on hundreds of thousands of public Facebook statuses on Facebook Pages ([via my Facebook Page Post Scraper](https://github.com/minimaxir/facebook-page-post-scraper)), from a very *diverse* variety of subreddits/Pages (which is necessary since some Pages will have *very* different reactions to a given text!). The network was also trained in such a way that the `rnn` layer is decontextualized in order to both improve training performance and mitigate authorial and temporal biases toward given reactions.
The `encode` function of reactionrnn returns the intermediate 256D output from the 'rnn' layer.
## Notes
* Keep in mind that the network is trained on modern (2016-2017) language. As a result, inputting rhetorical/ironic statements will often yield love/wow responses and not sad/angry.
* If a text sequence is >140 characters, reactionrnn will only use the first 140 characters.
* If you do use `encode` on multiple texts, I strongly recommend using [principal component analysis](https://en.wikipedia.org/wiki/Principal_component_analysis) to both reduce the high dimensionality of the text (i.e to 30-50D) and align the returned encoded texts. (see reactionrnn demos on how to implement PCA in Python and R)
* A GPU is not required to use reactionrnn.
## Future Plans for textgenrnn
* A web-based implementation using Keras.js (works especially well due to the network's small size)
* A larger pretrained network which can accommodate longer character sequences and a more indepth understanding of language, creating better/more robust reaction predictions. This may be released as a commercial product instead, if any venture capitalists are interested.
## Maintainer/Creator
Max Woolf ([@minimaxir](http://minimaxir.com))
*Max's open-source projects are supported by his [Patreon](https://www.patreon.com/minimaxir). If you found this project helpful, any monetary contributions to the Patreon are appreciated and will be put to good creative use.*
## Disclaimer
reactionrnn is not supported by nor endorsed by Facebook.
## License
MIT