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MixedEmotions

MixedEmotions

The MixedEmotions platform is a Big Data Toolbox for multilingual and multimodal emotion analysis. It is built around stand alone docker modules with an orchestrator that links the modules into analysis workflows utilising MESOS for scalable cloud deployment. Core capabilities include emotion extraction from text, audio and video with many other capabilities, such as sentiment analysis, social network analysis, entity detection and linking and sophisticated data visualisation.

Contact

[email protected]

Citation

if you use any of the modules, please cite the following paper: http://ieeexplore.ieee.org/abstract/document/8269329/

Demonstration Site

http://mixedemotions.insight-centre.org/

Modules

The MixedEmotions platform has been developed with several modules in it. Those modules provide diverse functionalities. Mainly, the toolbox is focused on extracting emoitions from different modalities: text, audio and video. However it also provides other kind of functionalities such as Social Network analysis, Knowledge graphs, entity linking and many others.

Some functionalities are provided with open source modules. Others are provided with proprietary modules.

The list of modules can be found in these tables below, with links pointing to resources where you can have more information about them.

Open Source Modules

Id Functionality Modality Language Source Download
m1 Sentiment Extraction Sentiment Extraction Text EN github Dockerhub
m2 Sentiment Extraction Sentiment Extraction Text EN, CS github [Dockerhub]
m4 Sentiment Extraction Sentiment Extraction Text EN, ES github Dockerhub
m5 Emotion recognition Emotion recognition from Text Text EN [github] Dockerhub
m6 Emotion recognition Emotion recognition from Audio Audio EN github Dockerhub
m7 Emotion recognition Emotion recognition from Text Text EN, ES, Multiple github Dockerhub
m8 Entity Extraction Entity Extraction Text ES github Dockerhub
m10 Entity Extraction and Linking Entity Extraction Linking Text EN github Dockerhub
m13 Topic Extraction Topic Extraction Text ES github Dockerhub
m16 Suggestion mining Suggestion Mining Text EN github Dockerhub
m20 Twitter media crawler Twitter Crawler Text n/a github n/a
m21 Fusion Fusion Text/Audio/Video n/a [github] [Dockerhub]
m22 Social Network Analysis Social Network Analysis graph n/a github Dockerhub
m25 Social semantic Knowledge graph KnowledgeGraph graph n/a github Dockerhub
m27 Emotion recognition from Video Emotion recognition from Video Video n/a github +info
m28 Analytics module “Kibi” - - github kibi
m32 Youtube crawler Youtube Crawler Text/Video n/a github n/a

Proprietary Modules

Id Functionality Modality Language Proprietary More info
m3 Sentiment Extraction Sentiment Extraction Text EN, IT ExpertSystem +info
m9 Entity Extraction Entity Extraction Text IT, EN ExpertSystem +info
m11 Topic Extraction Topic Extraction Text IT, EN ExpertSystem +info
m12 Topic Extraction Topic Extraction Text EN NUIG +info
m15 Entity Linking Entity Extraction Linking Text IT, EN ExpertSystem +info
m17 Speech to text Speech To Text Audio EN Phonexia +info
m18 Machine translation Machine Translations Text CS, ES, DE, IT NUIG +info
m23 Emotion recognition from Audio Emotion recognition from Audio Audio DE, EN, CS Phonexia +info
m24 Recommendation engine Text EN ExpertSystem +info
m28 Age estimation from audio Age Estimation Audio n/a Phonexia +info
m29 Gender identification from audio Gender Identification Audio n/a Phonexia +info

Custom Module

You can use your REST service or your own Docker module within the platform. For how to use a Docker module within the platform check the wiki

Orchestrator

Additionally an open source orchestrator has been developed as an starting point on using the MixedEmotions Toolbox. You can find it here.

More information

More information about the platform can be found on this project's wiki page.

Partners

Partner Country
NATIONAL UNIVERSITY OF IRELAND, GALWAY Ireland
UNIVERSIDAD POLITECNICA DE MADRID Spain
UNIVERSITAT PASSAU Germany
EXPERT SYSTEM S.P.A. Italy
PARADIGMA DIGITAL SL Spain
VYSOKE UCENI TECHNICKE V BRNE Czech Republic
SINDICE LIMITED Ireland
DEUTSCHE WELLE Germany
PHONEXIA SRO Czech Republic

Acknowledgement

This development has been funded by the European Union through the MixedEmotions Project (project number H2020 655632), as part of the RIA ICT 15 Big data and Open Data Innovation and take-up programme.

MixedEmotions

EU

http://ec.europa.eu/research/participants/portal/desktop/en/opportunities/index.html

MixedEmotions 's Projects

but_sentiment icon but_sentiment

Java wrapper around several sentiment analysis tools, that was created for MixedEmotions project, created by BUT.

json-ld_schema icon json-ld_schema

JSON-LD based format for representation of emotion, sentiment, entities and other results of language analysis in the MixedEmotions project.

kibi icon kibi

Kibi is a friendly - kept in sync - Kibana fork which add support for joins across indexes and external sources, tabbed navigation interface and more

kibi_radar_vis icon kibi_radar_vis

A Radar (or Spider) Chart plugin for Kibi 0.3.x+ or Kibana 4.3.x+ free as in beer and speech, enjoy! Sign up to our mailing list on http://siren.solutions for the most exciting updates about Kibana modding :)

knowledge-graph icon knowledge-graph

Creates knowledge graph from information processed by "Entity Extraction and Linking" module, and "Emotion Recognition from Text" module

marathon_configurations icon marathon_configurations

Marathon configuration files for the different MixedEmotions modules that have been dockerized and uploaded to MixedEmotions' Dockerhub.

nuig-sentiment icon nuig-sentiment

A python based sentiment predictor for tweets. Uses LSTM and pre-trained embeddings.

nuig-suggestion icon nuig-suggestion

Predicts whether a given text contains a suggestion or not. Given text can be a tweet or a sentence.

orchestrator icon orchestrator

An example orchestrator to interact with the MixedEmotions' modules.

scaner icon scaner

Social Context Analysis aNd Emotion Recognition

senpy icon senpy

A sentiment and emotion analysis server in Python

twitter_crawlers icon twitter_crawlers

MixedEmotions module that connects to the Twitter Stream API in order to retrieve Tweets regarding certain keywords or phrases

up_emotions_audio icon up_emotions_audio

This module aims to extract emotions from audio. The input argument is either an uploaded audio/video file to the server or a URL. The output is the predicted emotion in terms of Arousal and Valence within the JSON-LD format.

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