Sentiment analysis

We like to think of sentiment analysis as teaching AI to pick up on mood. It’s the text equivalent of watching someone’s face to see if they’re smiling or frowning. She reads words, then guesses whether the tone feels good, bad, or somewhere in between.

Polarity

The most common trick is polarity. It’s a fancy way of saying positive versus negative. If we say “This code runs beautifully,” she puts that in the positive bucket. If we say “This code crashes constantly,” she puts it in the negative one. Neutral words—“The program compiles”—get their own lane. Think of it like a three-way light switch: on, off, or middle.

Lexicons

Sometimes we hand her a dictionary of moods. These are lexicons, lists of words tied to sentiment scores. “Amazing” might score +2. “Terrible” gets -3. When she sees a sentence, she sums up the numbers. The total leans one way or another. It’s not perfect, since “This bug is sick” can throw her off, but it’s a quick way to scale.

Classification Models

Other times we skip the dictionary and train her on piles of labeled text. These are classification models. Feed her a thousand reviews tagged happy or angry, and she learns patterns we wouldn’t think to write down. She may notice “never again” is a bad sign even if “never” and “again” look harmless alone. This is slower to set up but sharper once she learns.

A coder’s musing

As coders, we can’t resist testing her. We throw in sarcastic lines just to see if she flinches. Sometimes she does. Sometimes she doesn’t. And when she shrugs at our best jokes, we’re reminded: mood is slippery, even for humans.