Sentiment Analysis
Sentiment analysis is the NLP task of determining the emotional tone or opinion expressed in text — positive, negative, or neutral. It is widely used in brand monitoring, customer feedback analysis, and social media analytics.
Understanding Sentiment Analysis
Sentiment analysis is a natural language processing task that identifies and extracts subjective opinions from text, classifying them as positive, negative, or neutral. It is one of the most widely deployed text classification applications, used by businesses to monitor brand perception on social media, analyze product reviews, gauge customer satisfaction, and track market sentiment in financial news. Modern sentiment analysis systems use transformer-based models fine-tuned on labeled opinion datasets, achieving near-human accuracy on standard benchmarks. Beyond simple polarity detection, advanced systems perform aspect-based sentiment analysis, identifying sentiment toward specific features or topics within a text. Multilingual models now extend sentiment capabilities across dozens of languages, enabling global brand monitoring at scale.
Category
Natural Language Processing
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Abstractive Summarization
Abstractive summarization generates new text that captures the key points of a longer document, rather than simply extracting existing sentences. It requires deep language understanding and generation capabilities.
Beam Search
Beam search is a decoding algorithm that explores multiple candidate sequences simultaneously, keeping only the top-k most promising at each step. It balances between greedy decoding and exhaustive search in text generation.
BERT
BERT (Bidirectional Encoder Representations from Transformers) is a language model developed by Google that reads text in both directions simultaneously. BERT revolutionized NLP by enabling deep bidirectional pre-training for language understanding tasks.
Bigram
A bigram is a contiguous sequence of two items (typically words or characters) from a given text. Bigram models estimate the probability of a word based on the immediately preceding word.
Byte Pair Encoding
Byte Pair Encoding (BPE) is a subword tokenization algorithm that iteratively merges the most frequent pairs of characters or character sequences. BPE is widely used in modern language models to handle rare words and multilingual text.
Corpus
A corpus is a large, structured collection of text documents used for training and evaluating natural language processing models. The quality and diversity of a training corpus significantly impacts model performance.
Extractive Summarization
Extractive summarization selects and combines the most important sentences directly from a source document to create a summary. It preserves the original wording but may lack the coherence of abstractive approaches.
Grounding
Grounding in AI refers to connecting a model's language understanding to real-world knowledge, data, or sensory experience. Grounded AI systems produce more factual and contextually relevant outputs.