Post-doctoral position : Deep Learning for opinion mining in human testimonials related to industrial accident

Contexte du poste

The Machine Learning team at the LITIS laboratory, the computer science laboratory of the University

of Rouen Normandy, is looking for a post-doctoral researcher on a 18-months contract, starting as

soon as possible. The position will be financed by the ANR research project CATCH (french acronym for

"Automatic Understanding of Human Sensors Testimonials"), which involves the R&D center of the

company Saagie1, specialized in B2B DataOps solutions, Atmo Normandie2, one of the approved French

air quality monitoring associations and LITIS.


Keywords

Deep learning – Natural Langage Processing – Sentiment Analysis / Opinion Mining


Scientific context

The ambition of the CATCH project is to propose artificial intelligence and deep learning tools to take

into account and automatically exploit the multitude of human testimonies related to an industrial

accident and its consequences on the environment and health. By involving the population in the

collection and analysis of data, particularly through social networks, and by providing effective means

for interpreting this data, the proposed solution should contribute to providing answers to the

worrying problem of industrial accidents and their consequences.

The overall objective is first to draw up a precise cartography of the nuisances in order to follow the

propagation and the evolution of the phenomena in time, and then to analyze and characterize the

sentiment of the population and its evolution throughout the crisis. To do so, we intend to exploit

testimonials collected on the ODO platform3 of Atmo Normandie, which combines these testimonies

with geographical information, in conjonction with data extracted from the micro-blogging platform

Twitter. Since these data are primarily texts, state-of-the-art approaches from the Natural Language

Processing (NLP) field are favored, in particular, self-supervised deep learning methods such as

Transformers4 that are known to be the most performant today for a wide range of NLP tasks5.

Description

The objective of this research work is twofold:

1. The automatic generation of a map of nuisances around the site of an industrial incident to

monitor the propagation and the evolution of the phenomena over time.

2. The automatic recognition of the population's perception and its evolution throughout the

crisis.

Related to these tasks, the post-doctoral researcher will be in charge of proposing solution for:

• extracting and linking twitter data with testimonials from the ODO dataset, which is fully

labelled and associates textual testimonies with geographical data. The interest in establishing

this link is to be able to enrich the data from the ODO platform to refine the mapping of

nuisances in real time. This could be achieved for example, by using pseudo-labelling

techniques6 or Constrative Representation Learning methods which have recently been applied

to text data7.

• detecting in all the testimonials collected from Twitter or from the ODO platform, the presence

(or absence) of several pre-identified emotions (e.g. surprise, fear, anger, sadness, disgust, etc.),

several of which can be expressed at the same time.

This research work will therefore involve being familiar with the state-of-the-art NLP deep learning

methods and in particular with their applications to sentiment analysis and opinion mining tasks. It will

also require experience with the use and exploitation of data from Twitter in a data science context.


Application

The successful applicant will:

• possess or be in the process of obtaining a Ph.D. in computer science or applied mathematics,

with a focus on machine learning or data mining.

• have strong programming skills (Java, Python, etc.) and in-depth understanding of statistics and

machine learning.

• have already worked with deep learning architecture dedicated to texts (RNNs, Transformers,

etc.) and/or images (CNNs, FCNs, GANs).

• have a productive publication record.

Your application should include:

• curriculum vitae

• statement of past research accomplishments, career goal and how this position will help you

achieve your goals

• two representative publications

contact information for at least two references

Comment postuler ?


Location : LITIS lab., University of Rouen Normandy, Rouen, France

Duration : 18-months, starting as soon as possible

Salary : ~2100€ / month (before income tax), including social security coverage in line with French

regulations

Applications : open from 01/09/2021 to 31/12/2021


Contact


Application must be sent to :

• Simon BERNARD, University of Rouen Normandy, simon.bernar d @univ-rouen.fr

• Clément CHATELAIN, INSA Rouen Normandy, clement.chatelain@insa-rouen.fr

• Alexandre PAUCHET, INSA Rouen Normandy, alexandre.pauchet@insa-rouen.fr