What do you mean by Multilingual Sentiment Analysis?
Multilingual sentiment analysis refers to the AI-driven process of extracting sentiment from data that contains multiple languages. This is achieved by utilizing language-specific machine learning (ML) models that are developed individually for each language. To create these models, a diverse corpus of manually tagged data is collected for each language. The key processes involved in multilingual sentiment analysis include:
- Part-of-speech (POS) tagger: This component is designed to identify various linguistic elements such as conjunctions, subordinate clauses, prepositions, and nouns specific to each language.
- Lemmatization: The lemmatization process aims to recognize and apply rules for conjugating nouns and verbs based on their gender or other linguistic features.
- Grammatical constructs: Specialized rules are built to define negations, amplifiers, and other grammatical constructs that help identify negative and positive words in different languages.
- Polarity determination: The polarity of words, indicating their negative or positive sentiment, is determined and assigned a value between -1 and +1. These values are aggregated to provide an overall sentiment analysis of the data.
Utilizing native language ML models is crucial because each language has its unique etymology, which influences its grammar rules and linguistic characteristics. For instance, Thai does not use full stops, Arabic is written from right to left, and German has gender-neutral pronouns. Using an English-based ML model to analyze multilingual data would apply rules applicable to English and yield inaccurate insights. This can result in failed or ineffective social and digital marketing campaigns, putting a strain on resources and diminishing returns.
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