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:
- Part-of-speech (POS) tagger: Built to identify conjunctions, subordinate clauses, prepositions and nouns for each language.
- Lemmatization: To recognize and apply rules of conjugating nouns and verbs based on gender.
- Grammatical constructs: Built to define negations and amplifiers to identify negative and positive words.
- Polarity: To determine the negative and positive polarity of words—between -1 and +1—which are aggregated to give the overall sentiment in the data.
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.