Which approach uses a predefined list of words to calculate a net sentiment score?

Which approach uses a predefined list of words to calculate a net sentiment score?

• A. Machine Learning
• B. Term Frequency (TF)
• C. Bag of Words
• D. Dictionary (Heuristic)

Answer: (D)

A predefined list of words with assigned sentiment values is a lexicon-based or dictionary-based approach to sentiment analysis. In this method, each word in the dictionary has a score indicating its positivity or negativity, and you compute the overall sentiment by aggregating those scores across the text—often by summing them, sometimes with adjustments for negations or intensifiers. This yields a clear net sentiment score and is inherently interpretable because you can see exactly which words contributed to the result. This fits best here because the description centers on using a fixed word list to derive the final sentiment, rather than letting a model learn from data or relying solely on word counts. Machine learning methods require labeled data and learn patterns from it, not a fixed dictionary. Term Frequency focuses on how often words appear without attaching sentiment values by itself, and Bag of Words represents text as word counts without encoding sentiment unless paired with a separate sentiment mapping.

A predefined list of words with assigned sentiment values is a lexicon-based or dictionary-based approach to sentiment analysis. In this method, each word in the dictionary has a score indicating its positivity or negativity, and you compute the overall sentiment by aggregating those scores across the text—often by summing them, sometimes with adjustments for negations or intensifiers. This yields a clear net sentiment score and is inherently interpretable because you can see exactly which words contributed to the result.

This fits best here because the description centers on using a fixed word list to derive the final sentiment, rather than letting a model learn from data or relying solely on word counts. Machine learning methods require labeled data and learn patterns from it, not a fixed dictionary. Term Frequency focuses on how often words appear without attaching sentiment values by itself, and Bag of Words represents text as word counts without encoding sentiment unless paired with a separate sentiment mapping.