Detecting polarity in news texts and comments

Text to analyze

This prototype assigns a sentiment score to a text based on how positive or negative it is. As a pre-processing step, it identifies the predominant language of a user inputted text and, if needed, translates it to English using the googletrans python library. The text is then analyzed using VADER-Sentiment-Analysis, which looks for various text features such as negations, exclamation marks, all caps, degree modifiers, sentiment-laden slang, and UTF-8 encoded emojis. A sentiment score is then assigned at both the text and sentence level. Finally, sentences are color-coded to visually convey the sentiment score. The technology could be used to identify positive and negative news comments and product reviews.