Sentiment analysis, also known as opinion mining, is a field of natural language processing (NLP) that analyzes people's opinions, sentiments, emotions, or attitudes towards something. In this guide, we will cover the basics of sentiment analysis and how to perform it using our NLP tools.

Basic Concepts

  • Sentiment: The overall feeling or attitude expressed in a piece of text.
  • Polarity: The direction of sentiment, which can be positive, negative, or neutral.
  • Subjectivity: The degree to which a statement is subjective rather than objective.

How to Perform Sentiment Analysis

  1. Data Collection: Gather the text data you want to analyze. This could be from social media, customer reviews, or any other source.
  2. Preprocessing: Clean and preprocess the text data by removing noise, such as HTML tags, special characters, and stop words.
  3. Feature Extraction: Convert the preprocessed text into a numerical representation that can be used by machine learning models.
  4. Model Training: Train a sentiment analysis model using labeled data.
  5. Prediction: Use the trained model to predict the sentiment of new, unseen text.

Example

Here's an example of a sentiment analysis result:

  • Text: "I love this product! It's amazing and I highly recommend it."
  • Sentiment: Positive
  • Polarity: 0.95
  • Subjectivity: 0.90

Resources

For more information on sentiment analysis, check out our Natural Language Processing documentation.

Images

Here are some examples of positive sentiment:

  • Smiling_Face
  • Thumbs_Up

And some examples of negative sentiment:

  • Frowning_Face
  • Thumbs_Down