Multilingual sentiment analysis is the AI-driven process of extracting sentiment from data containing several languages. It is achieved through native language machine learning (ML) models built individually for different languages. A highly varied corpus of manually tagged data is gathered for every language to develop these models. Key processes include:
A native language model is important because every language has its own etymology, which affects grammar rules. For example, there are no full stops in Thai, Arabic is written right to left and German has gender-neutral pronouns. If an English machine learning model is used to analyze multilingual data, it will use rules applicable to that language and provide incorrect insights. This can lead to failed or ineffective social and digital marketing campaigns that tax resources and reduce return.
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