Shoppers Sentiment Analysis

Degree Course: 
Electronic/Computer science engineer (Information-engineering track)
Department of Information Engineering - UNIVPM

The advent of Social Media has enabled everyone with a smartphone, tablet or computer to easily create and share their ideas, opinions and contents with millions of other people around the world. Recent years have witnessed the explosive popularity of image-sharing services such as Facebook, Instagram and Flickr. These data do not only reflect people social lives, but also express their opinions. Social media represent a rich source of knowledge to find information on consumer demand . The reason that sentiment analysis is becoming popular for retail and technology businesses is because customers increasingly express their desires, thoughts, preferences and frustrations online. With sentiment analysis, businesses have the opportunity to find out what their customers are saying and how they feel
about the products they’re offering. Using social media, it's now possible for retailers and tech companies to understand the sentiment of their customers in real time, finding out how they feel about the products on store shelves, store layouts and commercials. Furthermore, sentiment analysis can tell businesses how deeply customers feel about products, as well as which features are responsible for those
feelings. The goal of this thesis is to to classify and estimate the sentiment related to social media data.
In this context, the candidate will be required to study, investigate and apply machine-learning and deep learning approaches for image processing and classification.

Aim: Applying machine learning and deep learning for automatically classifying the sentiment of social media data

Supervisors: Prof. Emanuele Frontoni, Marina Paolanti

Start: Sept, 2018

Expected graduation: Feb/July, 2019

Skills that will be acquired: Programming (Python, MATLAB)

Contacts: {e.frontoni,m.paolanti}

Academic Tutor: 
Emanuele Frontoni