multi_lingual_sentiment_analyzer

Overview

This model is a high-performance multilingual sentiment classifier fine-tuned on the XLM-RoBERTa architecture. It is designed to detect emotional polarity in text across 100+ languages, categorizing inputs into Negative, Neutral, or Positive sentiments. It is particularly robust against code-switching and informal linguistic structures common in social media data.

Model Architecture

The model is based on XLMRobertaForSequenceClassification, a transformer-based encoder model.

• Backbone: XLM-R (Base)
• Parameters: ~270M
• Training Objective: Cross-Entropy Loss with Label Smoothing
• Input Processing: SentencePiece tokenization with a shared multilingual vocabulary.

The classification head consists of a linear layer applied to the representation of the <s> (start-of-sentence) token, formulated as:

Intended Use

• Global Brand Monitoring: Analyzing customer feedback across multiple regions in real-time.
• Social Media Analytics: Tracking public sentiment trends on global platforms.
• Support Ticket Triage: Automatically routing urgent negative feedback to specialized teams.

Limitations

• Sarcasm Detection: Like many transformer models, it may struggle with highly nuanced or culturally specific sarcasm.
• Context Length: The maximum sequence length is limited to 512 tokens.
• Low-Resource Languages: While multilingual, performance may be lower for languages with minimal training data in the original XLM-R corpus.