AffectInTweets

Finding the intensity of emotions in tweets (SemEval 2018 Task 1 Participant)

Introduction

Existing emotion analysis tools generally classify a given text into predefined emotion categories (positive, negative, or angry, cheerful, sad, etc.). However, knowing the intensity of emotion expressed in a given text would be useful for many applications such as brand and product perception tracking. In this study, a system was developed to measure the intensity of emotion in tweets by using shared datasets in the workshop named Affects in Tweets took place in SemEval 2018 - Task 1.

In the data set there are anonymous tweets and affective intensities between 0 and 1 determined by annotators for each emotion (anger, fear, fun and sadness). In this study, a model based on bidirectional LSTM networks, which provides highly successful results in natural language processing tasks is examined for finding emotion intensities of tweets. The results show that there is a significant increase in the performance when LSTM based model initialized with a pretrained model which is trained on a sentiment140 dataset and lexicon based features extracted from emoton lexicons are also utilized.

A simple user interface is provided to try the models. Follow the instructions below to run web based application.

A screenshot from the application

Experimental Results

• Performance comparisons of the models in terms of Pearson Corelation *

Method	Joy	Anger	Sadness	Fear	Valence
Bidirectional LSTM	0.49	0.35	0.47	0.49	0.32
Bidirectional LSTM + Lexicon Features	0.54	0.43	0.47	0.55	0.51
Bidirectional LSTM with pretraining	0.62	0.48	0.63	0.58	0.68
Bidirectional LSTM with pretraining + Lexicon Features	0.6	0.5	0.64	0.55	0.71

System Requirements

You should run the system on a machine where ptyhon-2.7 has been installed. The following packages are required to run the system.

numpy=1.13.1
gensim==3.0.0
scipy==0.19.1
nltk==3.2.4
tensorflow==1.2.1
keras==2.0.6
tornado==4.5.1

Running the program

The program has a web based user interface. You can run the web application just running the following command:

python src/web.py

The system will load required models to the memory and it will be ready when you see the following output in the console:

Loading emotion models...
Done.

Now you can reach the user interface through a web browser from following url:

http://localhost:8891

To run the program from command line run the following code:

python src/affect_predictor.py "tweet to be analyzed"

You can also use the program within python code using programming API:

from affect_predictor import AffectPredictor
AffectPredictor().predict("tweet to be analyzed")

If you want to train models from scratch, you can use the following command:

python src/affect_model_trainer.py