Concept-Level Sentiment Analysis with SenticNet

This project is in the context of sentiment analysis or opinion mining. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial prediction.

The candidate will focus on improving the current version of the Sentic API ([url removed, login to view]), a publicly available resource for concept-level sentiment analysis. Possible improvements are listed below.

1. KNOWLEDGE EXPANSION

Enriching SenticNet with new concepts.

Possible ways to do this are:

a) write some code (preferably in python but java also can) to import concepts from other resources, e.g., Bing Liu's Opinion Lexicon, MPQA Subjectivity Lexicon, SentiWordNet, Harvard General Inquirer, LIWC, etc.

b) crowdsourcing, e.g., through surveys, quizzes or games

c) an ensemble of the above

2. CONSISTENCY CHECK

Finding errors in SenticNet, e.g., in terms of polarity or semantics.

Possible ways to do this are:

a) do it manually

b) use the same code/resources mentioned before to find clashes (e.g., positive polarity in SenticNet but negative in SentiWordNet)

c) an ensemble of the above

3. FEATURE EXTENSION

Enriching SenticNet w/ new add-ons.

Possible ways to do this are:

a) add POS tags

b) add mood tags

c) add category tags

Possible extra activities for the candidate include working on any of the modules of our model for concept-level sentiment analysis ([url removed, login to view]).

Research

Project ID: #10000028

About the project