Datasets

Dataset for Task 1:

Opinion dataset with classes (Noise, objective, Positive, Negative, neutral sentiment, question, ads, miscellaneous) 

• Dataset length - 5000 for each social media (twitter and reddit) 

• This dataset has three level-annotations:

Level 1: It has three classes NOISE, OBJECTIVE, SUBJECTIVE and these three classes are marked with 0,1,2 respectively.

Level 2: It divides the SUBJECTIVE class further into three categories: NEUTRAL, NEGATIVE,POSITIVE and these are marked with 0,1,2 respectively.

Level 3: It divides the NEUTRAL class further into Four categories: NEUTRAL SENTIMENTS, QUESTIONS, ADVERTISEMENTS, MISCELLANEOUS and these are marked with 0,1,2,3 respectively.

A post which is in QUESTIONS class will have     Level 1 marking - 2

                                                 Level 2 marking - 0

                                                 Level 3 marking - 1

A post which is in OBJECTIVE class will have     Level 1 marking - 1

                                                 Level 2 marking -[Blank]

                                                 Level 3 marking -[Blank]

A post which is in NEGATIVE class will have     Level 1 marking - 2

                                                Level 2 marking - 1

                                                Level 3 marking - [Blank]

Dataset for Task 2:

QnA Dataset.

• Queries and comments

• length - approx 30k for reddit dataset

• marked as relevant and not relevant.

• Here every Qs is given with respective comments and relevant score/likes.

• The column "Relevant" contains binary labels -- relevant or not 

Training data: Mailed to the registered candidates

Test Data: Mailed to the registered candidates.