Multilingual Sentiment Analysis

What is multilingual sentiment analysis?

 Multilingual sentiment analysis is the AI-driven process of extracting sentiment from data containing several languages. Also called multilingual opinion mining, it’s a subset of social listening that involves the identification, extraction, analysis and labeling of customers' feelings and opinions expressed on social media across multiple languages. It is achieved through native language machine learning (ML) models built individually for different languages, taking into account the unique grammar, syntax and cultural nuances of each language. Benefits of multilingual sentiment analysis: 

• Global and real-time insights: Review customer sentiment from a diverse audience, providing a broader understanding of global customer opinions and helping brands break down language barriers to gain valuable insights in real-time. 
• Improved customer experience: Allows brands to tailor their responses and strategies according to the sentiments of customers speaking different languages. 
• Competitive advantage: Helps in identifying social media trends and sentiments across different markets, enabling better decision-making and strategy formulation. 
• Cultural relevance: Emotions and customer behavior are heavily influenced by language and culture, so sentiment analysis in English alone is not enough for organizations with an international customer base. 

Now that you understand the definition of multilingual sentiment analysis, let’s jump into how multilingual sentiment analysis works on social media.

How multilingual sentiment analysis works on social media 

Multilingual sentiment analysis uses natural language processing (NLP) and machine learning to analyze text data from social media in multiple languages.  Here's how it works: 

• Part-of-speech (POS) tagging: Identifying conjunctions, subordinate clauses, prepositions and nouns for each language. 
• Lemmatization: Recognizing and applying rules of conjugating nouns and verbs based on gender. 
• Grammatical constructs: Defining negations and amplifiers to identify negative and positive words. 
• Polarity: Determining the negative and positive polarity of words, which are then aggregated to give the overall customer sentiment. 

 NLP combines computer science, AI and linguistics to enable machines to understand human language. Machine learning models are trained with annotated texts to identify patterns and create sentiment scores. These scores can be:  

• Rule-based (using programmed rules) 
• Automatic (using machine learning) 
• Hybrid (combining both approaches)