Applications of N-grams in Sentiment Analysis

  1. Capturing Negation:

    Bigrams like “not good” or “not happy” carry negative sentiment, even though the individual words may carry neutral or positive connotations.

  2. Intensification or Diminishment:

    Phrases like “very good” or “extremely happy” indicate stronger positive sentiment compared to just “good” or “happy”. Conversely, “a bit disappointed” expresses a milder negative sentiment.

  3. Contextual Understanding:

    Bigrams help differentiate between neutral and sentiment-laden phrases. For instance, “customer service” in certain contexts may carry a sentiment distinct from the individual words “customer” and “service”.

  4. Idioms and Colloquial Expressions:

    N-grams can capture idiomatic expressions, such as “piece of cake” (positive sentiment) or “rain on my parade” (negative sentiment).

  5. Domain-specific Understanding:

    Domain-specific n-grams capture industry-specific language patterns. For example, in movie reviews, bigrams like “plot twist” or “special effects” carry distinct sentiment within the film industry context.


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