Master internship and PhD position in Conditional Random Field learning for Sentiment Analysis in Phone Conversations

Master internship and PhD position in Conditional Random Field learning for Sentiment Analysis in Phone Conversations

Telecom ParisTech (http://www.telecom-paristech.fr/eng/)
37 rue Dareau, 75014 Paris - France

Advisors:
Chloé Clavel  (http://clavel.wp.mines-telecom.fr/)
Slim Essid  (http://perso.telecom-paristech.fr/~essid/)

Starting date: Anytime from February to June 2015

Funding: Secured with the Telecom ParisTech Machine Learning for Big Data Chaire (http://machinelearningforbigdata.telecom-paristech.fr)

Keywords: Sentiment Analysis, Opinion Mining, Machine Learning, Conditional Random Fields, Natural Language Processing, Audio and Speech Processing

Applications are invited for a 3 to 6 month master internship to be continued by work towards a PhD for a duration of 36 months. Outstanding candidates will be considered to start the PhD work immediately, without doing the internship.

Topic:
Sentiment analysis and opinion mining have gained an increasing interest with the explosion of textual content conveying users’ opinions (e.g. film reviews, forum debates, tweets). Hence, natural language processing researchers have dedicated a great deal of effort into the development of methods amenable to opinion detection in such texts, though often simplifying the problem to one of classification over the valence (positive vs negative) and intensity axes. As for sentiment analysis in speech signals, there have been hardly any attempts. Further challenges are posed in this case where not only should the special features of spoken language be taken into account, but also prosodic features and the potential errors of automatic speech recognition systems.
The research work planned will focus on the development of sentiment analysis methods in the context of phone conversations. The privileged research direction will consist in exploiting the appraisal theory adapted to the verbal content (as defined by psycho-linguists) in order to create effective computational models of evaluative expressions. In particular, Conditional Random Fields will be considered with feature functions encoding the semantic rules usually used for our task.

IDEAL CANDIDATE:
Master’s student or Master’s degree with background in
-    Machine learning / pattern recognition
-    Speech processing, natural language processing
-    Excellent programming skills (Python, Java, C/C++)
-    Good English level

APPLICATIONS :
To be sent to chloe.clavel@telecom-paristech.fr, slim.essid@telecom-paristech.fr,:
-    Curriculum Vitae
-    Statement of interest (in the body of the email)
-    Academic records
-    List of references

Incomplete applications will not be considered.
 


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