Identifying gender bias in blockbuster movies through the lens of machine learning. (arXiv:2211.12504v1 [cs.CL])

The problem of gender bias is highly prevalent and well known. In this paper,
we have analysed the portrayal of gender roles in English movies, a medium that
effectively influences society in shaping people’s beliefs and opinions. First,
we gathered scripts of films from different genres and derived sentiments and
emotions using natural language processing techniques. Afterwards, we converted
the scripts into embeddings, i.e. a way of representing text in the form of
vectors. With a thorough investigation, we found specific patterns in male and
female characters’ personality traits in movies that align with societal
stereotypes. Furthermore, we used mathematical and machine learning techniques
and found some biases wherein men are shown to be more dominant and envious
than women, whereas women have more joyful roles in movies. In our work, we
introduce, to the best of our knowledge, a novel technique to convert dialogues
into an array of emotions by combining it with Plutchik’s wheel of emotions.
Our study aims to encourage reflections on gender equality in the domain of
film and facilitate other researchers in analysing movies automatically instead
of using manual approaches.