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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 thateffectively influences society in shaping people's beliefs and opinions. First,we gathered scripts of films from different genres and derived sentiments andemotions using natural language processing techniques. Afterwards, we convertedthe scripts into embeddings, i.e. a way of representing text in the form ofvectors. With a thorough investigation, we found specific patterns in male andfemale characters' personality traits in movies that align with societalstereotypes. Furthermore, we used mathematical and machine learning techniquesand found some biases wherein men are shown to be more dominant and enviousthan women, whereas women have more joyful roles in movies. In our work, weintroduce, to the best of our knowledge, a novel technique to convert dialoguesinto 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 offilm and facilitate other researchers in analysing movies automatically insteadof using manual approaches.

Identifying gender bias in blockbuster movies through the lens of machine learning