Analyse Sentiment

The analyze().sentiment module performs sentiment analysis on text using the specified lexicon dictionary. It utilises a bag-of-words approach, analyzing the occurrence of terms without considering their order.

For score-based lexicons like VADER_v2014, it sums the scores of matched terms and returns a single float/integer value reflecting overall sentiment. Higher scores indicate more positive/negative sentiment.

from sentibank.utils import analyze

# Analyse the sentiment
analyze = analyze()
text = "I am excited and happy about the new anouncement!"
result = analyze.sentiment(text=text, dictionary="VADER_v2014")

4.1

For label-based dictionaries like HarvardGI_v2000, it counts matched terms per sentiment category and returns a dictionary of those label counts. The category with the most matches indicates the dominant overall sentiment.

from sentibank.utils import analyze

# Analyse the dictionary
analyze = analyze()
text = "I am excited and happy to make this anouncement to our shareholders."
result = analyze.sentiment(text=text, dictionary="MASTER_v2022")

{'Negative': 0,
 'Uncertainty': 0,
 'Constraining': 0,
 'Positive': 2,
 'Litigious': 0,
 'Weak_Modal': 0,
 'Strong_Modal': 0}