Sentiment Analysis: Analyzing Emotions in Text

Glossary/S/Sentiment Analysis

What is Sentiment Analysis?

Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique used to determine the sentiment or emotional tone expressed in a piece of text. It involves analyzing textual data, such as customer reviews, social media posts, and survey responses, to understand the underlying sentiment, whether it is positive, negative, or neutral.

The goal of sentiment analysis is to extract subjective information from text and interpret the sentiment conveyed by the author. By automatically classifying and quantifying sentiments, organizations can gain insights into public opinion, customer feedback, and brand perception. This information is valuable for making informed decisions, improving products and services, and enhancing customer experiences.

Related terms

Data Labeling/Annotation

Data labeling, also known as data annotation, is the process of assigning meaningful tags or annotat...

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on enabli...

Not to be confused with:

Data Labeling/Annotation

Data labeling, also known as data annotation, is the process of assigning meaningful tags or annotat...

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on enabli...

Data Labeling/Annotation

Data labeling, also known as data annotation, is the process of assigning meaningful tags or annotat...

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on enabli...

Data Labeling/Annotation

Data labeling, also known as data annotation, is the process of assigning meaningful tags or annotat...

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on enabli...

Machine learning algorithms are trained on labeled data, where human annotators assign sentiments to text samples. These algorithms learn patterns and relationships between words and sentiments, allowing them to classify new text based on the learned knowledge.

Sentiment analysis finds applications in various industries. For example, in social media monitoring, sentiment analysis helps track brand reputation and public opinion.

However, sentiment analysis is a challenging task due to the complexities of language, sarcasm, context, and cultural nuances. It requires continuous refinement and adaptation to different domains and languages. Additionally, multilingual sentiment analysis is an active area of research as sentiments can vary across different cultures and languages.

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