Multilingual sentiment analysis is the AI-driven process of extracting sentiment from data containing several languages. It relies on native-language machine learning (ML) models, so there’s a separate model for each language.​​ 

Teams train each model on a varied corpus of human-tagged data in that language. Key processes include:​​ 

  • Part-of-speech (POS) tagger: Identifies conjunctions, subordinate clauses, prepositions and nouns for each language.​​ 
  • Lemmatization: Reduces each word to its base form (lemma), accounting for gender, tense and conjugation rules in that language.​​ 
  • Grammatical constructs: Defines negations and amplifiers to identify negative and positive words.​​ 
  • Polarity: Determines the negative and positive polarity of words—between -1 and +1—which are aggregated to calculate the overall sentiment in the data.​​ 

Native-language models aren’t just a nice-to-have; they are essential. Every language operates on its own grammar, script and writing conventions. For example, there are no full stops in Thai, Arabic is written right to left and German assigns a grammatical gender to every noun. Dedicated models for French sentiment analysis and Spanish sentiment analysis learn each language’s own rules.​​ 

Running an English machine learning model on multilingual data skews your analysis. It forces English rules onto other languages, delivering inaccurate insights. That leads to failed or ineffective social and digital marketing campaigns that tax resources and reduce return.​​ 

Some systems translate text into English first or use multilingual pretrained models trained on many languages at once, but translation risks losing idioms and slang. Social posts often mix languages in a single message (code-switching), one of the sentiment analysis challenges every model has to handle.​​ 

Extract key business insights from global conversations without losing the nuances of language. Try Sprout Social‘s Smart Inbox sentiment classification free today.​​