"In the realm of Natural Language Processing (NLP), sentiment analysis is a powerful tool that unlocks the emotional undercurrents of text data. It delves beyond the literal meaning of words, aiming to classify the sentiment expressed as positive, negative, or neutral. This allows us to gauge public opinion on social media, analyze customer reviews, or understand the overall tone of a news article. By deciphering the emotional sentiment embedded within language, NLP empowers machines to make sense of the human experience in a nuanced way, opening doors for more empathetic and insightful applications."- Gemini 2024

Examples processing a sentence

The quality of a sentiment analysis tool can vary considerably. We start to see significant variance with sentences that are "difficult" to guage. For instance, sarcasm requires a deeper understanding of a language, and looking at statements in context. It also helps to have familiarity with the speaker - something an NLP model will likely not have.

Input = "I just love standing in line."

Example code with NLTK

In the output of this example code, we see that our code also incorrectly rates the statement as postive with a score of 51%.

import nltk
nltk.download('vader_lexicon')

from nltk.sentiment import SentimentIntensityAnalyzer


sent = "I just love standing in line."

sa = SentimentIntensityAnalyzer()
scores = sa.polarity_scores(sent)

for k, v in scores.items():
    print(f'{k}\t{v}')