On the Causal Nature of Sentiment Analysis
Zhiheng Lyu Zhijing Jin Fernando Gonzalez Rada Mihalcea Bernhard Schoelkopf Mrinmaya Sachan
Sentiment analysis (SA) aims to identify the sentiment expressed in a text,
such as a product review. Given a review and the sentiment associated with it,
this paper formulates SA as a combination of two tasks: (1) a causal discovery
task that distinguishes whether a review "primes" the sentiment (Causal
Hypothesis C1), or the sentiment "primes" the review (Causal Hypothesis C2);
and (2) the traditional prediction task to model the sentiment using the review
as input. Using the peak-end rule in psychology, we classify a sample as C1 if
its overall sentiment score approximates an average of all the sentence-level
sentiments in the review, and C2 if the overall sentiment score approximates an
average of the peak and end sentiments. For the prediction task, we use the
discovered causal mechanisms behind the samples to improve the performance of
LLMs by proposing causal prompts that give the models an inductive bias of the
underlying causal graph, leading to substantial improvements by up to 32.13 F1
points on zero-shot five-class SA. Our code is at
https://github.com/cogito233/causal-sa