What is Sentiment Analysis? - Definition & Meaning

Definition

Sentiment analysis (opinion mining) is an NLP task that determines the emotional tone, attitude, or opinion in text — typically positive, negative, or neutral, sometimes with granularity (anger, sadness, joy).

Technical explanation

Sentiment analysis ranges from binary (pos/neg) to multi-class or regression (sentiment score). Methods: lexicon-based (summing word scores), machine learning (naive Bayes, SVM on features), and deep learning (LSTM, BERT). Aspect-based sentiment analyzes sentiment per topic ("battery": negative, "screen": positive). Challenges: sarcasm, negation ("not good"), and domain-specific language. Modern models use transformers and fine-tuning on labeled reviews or social data.

How AVARC Solutions applies this

AVARC Solutions implements sentiment analysis for customer feedback, review aggregation, social media monitoring, and support ticket triage. We train models on Dutch and domain-specific datasets for accurate results.

Practical examples

• A review aggregator computing sentiment per product category and showing trends over time.
• A social media monitor tracking sentiment around brands and campaigns for reputation management.
• A customer service prioritizing tickets with negative sentiment for faster escalation.

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