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Text polarity detection is the task of deciding whether a piece of text expresses a favorable or unfavorable opinion, such as determining whether a product review praises or criticizes the item.

Text Polarity Detection

A sentiment analysis system can first use a linguistic tagger to mark parts of speech in a review, such as adjectives and nouns, and then pass that annotated text to a sentiment classifier. This extra structure helps the classifier focus on opinion-bearing words like "excellent" instead of treating every word as equally important.

Sentiment Analysis with Grammatical Tags

What is the goal of sentiment classification for a text review?

True or False: The sentence "What a fantastic sandwich!" would likely be classified as highly positive sentiment.

Predicting whether a piece of text expresses favorable or unfavorable opinion is called _____.

Match each review to the sentiment label it best fits.

Order the steps a spam filter follows to decide whether a message is junk.

Question:
In a few sentences, explain sentiment classification and why "The battery pack works perfectly and saved my trip!" and "The battery pack died on day one, which was frustrating." belong to opposite sentiment groups.

Sample answer:
Sentiment classification is a text analysis task that assigns an attitude label, often positive or negative, to a sentence or review. The first example is positive because phrases like "works perfectly" and "saved my trip" express satisfaction and a helpful outcome. The second example is negative because "died on day one" and "frustrating" describe failure and disappointment. The wording in each sentence reveals the overall tone and therefore its sentiment label.

Key points:
- Sentiment classification labels text by the attitude it expresses
- The first sentence conveys a favorable experience
- The second sentence conveys an unfavorable experience
- Specific wording provides the clue for the label

Rubric:
Full credit: clearly explains sentiment classification and justifies both labels using the wording in each sentence. Partial credit: gives the definition but analyzes only one sentence or gives incomplete reasons. No credit: missing or incorrect explanation.

Understanding Sentiment Labels in Short Reviews

Case context:
A team is building a system that reads customer reviews about restaurants and predicts whether the writer is pleased or displeased with the experience.

Question:
A review says: 'I was disappointed from the start—the soup was cold and the server never came back.' Using sentiment classification, how should this review be labeled, and which words support that label?

Sample answer:
This review should be labeled negative. The phrase 'I was disappointed from the start' clearly shows dissatisfaction, and 'the soup was cold' plus 'the server never came back' describe problems with the service and food. Those details indicate an unfavorable opinion.

Key points:
- The review should be classified as negative
- 'I was disappointed' signals dissatisfaction
- Cold food and poor service support the negative label
- Sentiment classification uses opinion-bearing language in the text

Rubric:
Full credit: correctly labels the review as negative and cites specific evidence from the text. Partial credit: correct label without supporting evidence. No credit: incorrect label.

Classify the tone of a customer restaurant review.

Question:
Briefly define sentiment classification and use one example review of a wireless speaker to illustrate it.

Sample answer:
Sentiment classification is the task of deciding whether a piece of text expresses a positive or negative opinion. For example, the review "This speaker has rich sound and works perfectly" would be classified as positive because it shows approval.

Key points:
- Sentiment classification identifies whether text is favorable or unfavorable.
- A correct example should match the label implied by the wording.

Rubric:
Full credit: gives an accurate definition and a correct illustrative example. Partial credit: gives only the definition or only a valid example. No credit: provides an incorrect definition or an example that does not match the sentiment.

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