ConvoKit is a toolkit for extracting conversational features and analyzing social phenomena in conversations. It includes several large conversational datasets along with scripts exemplifying the use of the toolkit on these datasets.
This change adds the PosNegIrony transformer based on the theory described in Linda Hutcheon's paper The Complex Functions of Irony and a demo using the r/Ohio and r/Cleveland subreddit corpus'. The transformer uses a sentiment-based approach, categorizing ironic utterances by the statistical deviation from the mean sentiment of the corpus. Then it applies a simple rule-based approach to categorize an ironic utterance as positive or negative irony by taking a measure of the sentiment of the comment itself and its replies.
Motivation and Context
This new transformer aims to capture and score the linguistic/literature theory of positive and negative irony. It requires the corpus to have utterances labeled with "/s" for ironic comments, so it is primarily for subreddit / Reddit corpus'.
How has this been tested?
This has been tested locally in a Jupyter Notebook using the demo code provided.
Other information
This was made in part of the requirement of A8 of INFO 4350.
Description
This change adds the PosNegIrony transformer based on the theory described in Linda Hutcheon's paper The Complex Functions of Irony and a demo using the r/Ohio and r/Cleveland subreddit corpus'. The transformer uses a sentiment-based approach, categorizing ironic utterances by the statistical deviation from the mean sentiment of the corpus. Then it applies a simple rule-based approach to categorize an ironic utterance as positive or negative irony by taking a measure of the sentiment of the comment itself and its replies.
Motivation and Context
This new transformer aims to capture and score the linguistic/literature theory of positive and negative irony. It requires the corpus to have utterances labeled with "/s" for ironic comments, so it is primarily for subreddit / Reddit corpus'.
How has this been tested?
This has been tested locally in a Jupyter Notebook using the demo code provided.
Other information
This was made in part of the requirement of A8 of INFO 4350.