From the course: Deep Learning with Python and Keras: Build a Model for Sentiment Analysis
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Approaches and challenges in sentiment analysis
From the course: Deep Learning with Python and Keras: Build a Model for Sentiment Analysis
Approaches and challenges in sentiment analysis
- [Narrator] In this movie, we'll discuss in a little more detail some of the common approaches to sentiment analysis. These are approaches that I briefly covered earlier. We'll also discuss some challenges with sentiment analysis. Approaches to sentiment analysis include: Rule-based sentiment analysis. Machine learning-based sentiment analysis. And hybrid sentiment analysis. Let's start by understanding how rule-based sentiment analysis works. This is where you'll set up human-crafted rules to help identify subjectivity, polarity, or subject matter of the text. You'll use some kind of system. Let's say words like happy, affordable, fast will be in the positive lexicon, and words like poor, expensive, and difficult will be in the negative lexicon. You'll then set up word scores for positive and negative words. Let's say scores from 5 to 10 denote positive words, scores from -1 to -10 denote negative words. Thus…
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