Sentiment Analysis
Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. Wikipedia
TextBlob
Textblob provied basic polarity and subjectivity scores.
Polarity ranges between -1.0 and 1.0 with positive scores indicating positive sentiemnt and negative scores indicating negative sentiment. The subjectivity score ranges from 0.0 for very objective to 1.0 for very subjective.
VADER
VADER (Valence Aware Dictionary and sEntiment Reasoner) is a library built for sentiment analysis and specifically social media text.
Below the compound score roughly corresponds the textblob's polarity with values ranging from 0.0 to 1.0, values greater the 0.5 being positive sentiment and less than 0.5 being negative sentiment. The pos, neu, and neg scores are ratios for proportions of text that fall in each category.