FastPrepClassify Reviews by Sentiment Word Occurrences

Classify Reviews by Sentiment Word Occurrences

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Problem statement

For every review, split on whitespace and count all token occurrences found in positiveWords and negativeWords. Repeated tokens count repeatedly. Matching is case-sensitive and punctuation remains part of a token.

Return positive when the positive count is greater, negative when it is smaller, and neutral when the counts are equal. A token present in both lexicons contributes to both counts.

Function

classifyReviews(reviews: String[], positiveWords: String[], negativeWords: String[]) → String[]

Examples

Example 1

reviews = ["good good bad","bad","plain"]positiveWords = ["good"]negativeWords = ["bad"]return = ["positive","negative","neutral"]

The batch contains positive, negative, and neutral reviews.

Example 2

reviews = ["great great poor"]positiveWords = ["great"]negativeWords = ["poor"]return = ["positive"]

Every matching occurrence contributes.

Constraints

  • 0 <= reviews.length <= 10000
  • The total input text length is at most 200000.

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public String[] classifyReviews(String[] reviews, String[] positiveWords, String[] negativeWords) {
    // Write your code here.
}
reviews["good good bad","bad","plain"]
positiveWords["good"]
negativeWords["bad"]
expected["positive", "negative", "neutral"]
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