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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

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Python module + R package to predict the reactions to a given text using a pretrained recurrent neural network.

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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 reactionrnn



react = 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