data

Subtask 1: Monolingual structured sentiment

This track assumes that we train and test on the same languages. For this we will use the following datasets:

1. norec (Norwegian professional reviews in multiple domains)
2. multibooked_ca (Catalan hotel reviews)
3. multibooked_eu (Basque hotel reviews)
4. opener_en (English hotel reviews)
5. opener_es (Spanish hotel reviews)
6. darmstadt_unis (English online university reviews)

Subtask 2: Cross-lingual structured sentiment

This track will instead train only on a high-resource language (English) and test on several languages.

Train:

1. opener_en

Test:

1. opener_es
2. multibooked_ca
3. multibooked_eu
4. Several surprise languages that will not be available until the evaluation phase.

That means that the cross-lingual models should be able to adapt quickly to new languages.

Data and formatting

We provide the data in json lines format.

Each line is an annotated sentence, represented as a dictionary with the following keys and values:

• 'sent_id': unique sentence identifiers

• 'text': raw text

• 'opinions': list of all opinions (dictionaries) in the sentence

Additionally, each opinion in a sentence is a dictionary with the following keys and values:

• 'Source': a list of text and character offsets for the opinion holder

• 'Target': a list of text and character offsets for the opinion target

• 'Polar_expression': a list of text and character offsets for the opinion expression

• 'Polarity': sentiment label ('negative', 'positive', 'neutral')

• 'Intensity': sentiment intensity ('average', 'strong', 'weak')

{
    "sent_id": "../opener/en/kaf/hotel/english00164_c6d60bf75b0de8d72b7e1c575e04e314-6",

    "text": "Even though the price is decent for Paris , I would not recommend this hotel .",

    "opinions": [
                 {
                    "Source": [["I"], ["44:45"]],
                    "Target": [["this hotel"], ["66:76"]],
                    "Polar_expression": [["would not recommend"], ["46:65"]],
                    "Polarity": "negative",
                    "Intensity": "average"
                  },
                 {
                    "Source": [[], []],
                    "Target": [["the price"], ["12:21"]],
                    "Polar_expression": [["decent"], ["25:31"]],
                    "Polarity": "positive",
                    "Intensity": "average"}
                ]
}

You can import the data by using the json library in python:

>>> import json
>>> with open("data/norec/train.json") as infile:
            norec_train = json.load(infile)