Targeted Sentiment Analysis on The Norwegian Review Corpus, NoReC_tsa.

NoReC_fine is a Norwegain dataset annotated for sentiment expression with targets and holder. See the github repository and paper for details.
NoReC_fine consists of professional reviews, a subset of the Norwegian Review Corpus NoReC.

Convert NoReC_fine sentiment targets to CoNLL

Our conversion scipt presented here, converts the NoReC_fine sentiment targets to CoNLL, for training models for Targeted Sentiment Analysis (TSA).

When doing Targeted Sentiment Analysis, we are interested in what is spoken positively or negatively about in each sentence, and what is the polarity of the sentiment. A sentiment target may have more than one opinion towards it. We count 1732 sentiment targets with more than one opinion towards it, and 402 of these have both positive and negative opinions. An example from the dataset:

{"sent_id": "004702-02-01", "text": "Forviklingskomedie med brodd og bismak .", 
    "opinions": [
        {"Source": [[], []], "Target": [["Forviklingskomedie"], ["0:18"]], 
                "Polar_expression": [["brodd"], ["23:28"]], "Polarity": "Positive", "Intensity": "Strong", "NOT": false, "Source_is_author": true, "Target_is_general": false, "Type": "E"}, 
        {"Source": [[], []], "Target": [["Forviklingskomedie"], ["0:18"]], "Polar_expression": [["bismak"], ["32:38"]], "Polarity": "Negative", "Intensity": "Standard", "NOT": false, "Source_is_author": true, "Target_is_general": false, "Type": "E"}]},

The annotations give that Forviklingskomedie is a sentiment target with two opinions towards it: "brodd", which is positive with strong intensity, and "bismak" which is negative with standard intensity. Our script resolves these situations to the polarity with the strongest total intensity weight. (In case of a tie, the last opinion polarity wins.) Our example target "Forviklingskomedie" is resolved to a positive sentiment target since the positive expression has the strongest intensity. The TSA CoNLL output for this sentence is therefore as follows:

#sent_id=004702-02-01
Forviklingskomedie	B-targ-Positive
med	O
brodd	O
og	O
bismak	O
.	O

The sentence id

The sentence id line starts with a '#' and has no tab in it. The first 6 digits are the document identification. The same document id is used in the NoReC metadata. Here, one may retrieve information about the document genre, author, year and rating of the reviewed entity.

To run the conversion script:

1. Clone the repo norec_fine git clone https://github.com/ltgoslo/norec_fine.git
2. Run python tsa_conll.py with optional arguments for the path to the NoReC_fine folder containing the json files, and for the path for the resulting CoNLL tab-separated files.

usage: tsa_conll.py [-h] [--jfolder JFOLDER] [--cfolder CFOLDER] [--intensities]

optional arguments:
  -h, --help            show this help message and exit
  --jfolder JFOLDER, -jf JFOLDER
                        Folder with train, dev and test norec fine json files. Default is 'norec_fine'
  --cfolder CFOLDER, -cf CFOLDER
                        Folder for saving converted conll files. Default is 'tsa_conll'
  --intensities, -i     Add this flag to add intensity to the polarity label

The result of the conversion as per posting this repository, is found in the tsa_conllsubfolder.

Conversion from conll to HF Datasets

conll_to_dataset.ipynb has a script for converting the conll-formatted TSA datasets into the HF Datasets Arrow-format. The contents of the resulting folders, can be loaded through datasets.load_from_disk.

from datasets import load_from_disk, DatasetDict, Dataset
load_from_disk(<arrow_folder>)

Sentiment intensity integers

The intensities annotated in NoReC_fine are converted as follows: {'Slight':1, 'Standard':2, 'Strong':3}. When multiple sentiment expressions have the same target, the values are added / subtracted. When a target receives equaly strong pos and neg sentiments, the last polarity in the sentence is assigned. This corresponds with an intuitive understanding of communicating sentiment in Norwegian, that the last sentiment expressed tends to be conclusive. The intensity is in these cases set to 1. Our conversion script does not output any "mixed" polarity classification, but this can be modified in the script.

Cite this work

If you use the NoReC_tsa dataset in your work, please cite as informed on the NoReC_fine github page.

Licencing

The licence for this derived version of NoReC_fine, is found on the NoReC_fine github page.