Titulo Estágio
Sentiment Analysis in Social Networks
Áreas de especialidade
Sistemas Inteligentes
Local do Estágio
CISUC
Enquadramento
The constant growth of platforms like Twitter, or Facebook, shows the importance of social media in nowadays people’s lives. We are now all connected to friends, family, and communities, all around the world, and share information on a daily basis. Different social networks have been created and popularized, like the above mentioned Twitter and Facebook, but also Instagram, LinkedIn, or Reddit. And even though the first social networks were mostly text-based, it has now become popular to share multimedia content, too, like photos, music, or video.
Mostly considered as an entertainment tool, social networks can be an important source of information, since relevant and valuable information is publicly shared among users, and may be used towards the resolution of challenges faced by individuals and companies. Considering their relevance, specially in information spread, they have gained increasing importance and are being widely studied in many fields of research, specially due to their multiple applications. However, multiple challenges arise, not only due to the amount of data, but also due to subjectiveness, as the content can be influenced by contextual, emotional, and social constraints, which are extremely demanding tasks for a machine to cope with.
In this internship it is proposed to research and implement learning models able to analyze sentiments in both text and images shared among social networks. The main idea is to capture different types of information (like text and images) in order to create a learning model with enhanced capabilities when regarding to such subjective task as sentiment analysis.
Objetivo
In this internship the student should study, propose, implement, and test models for Sentiment Analysis in Social Networks.
To achieve this goal, the following objectives will be pursued:
- Study the state of the art
- Study the available datasets and sources of online information (API)
- Study the available frameworks for model development
- Define, implement, and fine tune the learning architecture
- Propose and deploy test setup
Plano de Trabalhos - Semestre 1
- Literature review
- Identification of datasets sources for model development
- Identification and study of available frameworks for model development
- Define the architecture of the system
- Start implementing the proposed approach
- Write intermediate report
Plano de Trabalhos - Semestre 2
- Implement the proposed solution and fine tune models
- Test and evaluate performance
- Write final report
Condições
This work should take place in the context of a research project in a CISUC lab.
Orientador
Catarina Helena Branco Simões da Silva e Joana Madeira Martins Costa
catarina@dei.uc.pt 📩