What is sentiment analysis, and how is it implemented?

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Sentiment Analysis is a Natural Language Processing (NLP) technique used to determine the emotional tone behind text, such as positive, negative, or neutral. It helps businesses analyze customer feedback, reviews, or social media posts to understand opinions and improve decision-making. For example, analyzing “The service was excellent” gives a positive sentiment, while “The product is terrible” gives a negative sentiment.

Implementation usually involves the following steps:

1. Data Collection – Gather text data from reviews, tweets, or feedback.

2. Text Preprocessing – Clean the text by removing stop words, punctuation, and applying stemming or lemmatization.

3. Feature Extraction – Convert text into numerical form using methods like Bag of Words, TF-IDF, or Word Embeddings (Word2Vec, GloVe, BERT).

4. Model Training – Train machine learning models (Naive Bayes, Logistic Regression, SVM) or deep learning models (LSTMs, Transformers).

5. Prediction & Evaluation – Classify new text into sentiment categories and measure accuracy using metrics like precision, recall, and F1-score.

Modern implementations often use pre-trained transformer models like BERT or GPT, which provide high accuracy in sentiment classification tasks.

Read More:

How does a transformer-based language model like GPT work?

What is tokenization in NLP?

Explain stemming and lemmatization.

What is word embedding, and what are Word2Vec and GloVe?

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