Sentiment Analysis also konwn as opinion mining or emotion AI is perhaps one of the most popular applications of natural language processing and text analytics with a vast number of websites, books and tutorials on this subject. Typically sentiment analysis seems to work best on subjective text, where people express opinions, feelings, and their mood. From a real-world industry standpoint, sentiment analysis is widely used to analyze corporate surveys, feedback surveys, social media data, and reviews for movies, places, commodities, and many more. The idea is to analyze and understand the reactions of people toward a specific entity and take insightful actions based on their sentiment.
Lexicon-based Sentiment Analysis techniques, as opposed to the Machine Learning techniques, are based on calculation of polarity scores given to positive and negative words in a document.
Sentiment polarity is typically a numeric score that’s assigned to both the positive and negative aspects of a text document based on subjective parameters like specific words and phrases expressing feelings and emotion. Neutral sentiment typically has 0 polarity since it does not express and specific sentiment, positive sentiment will have polarity > 0, and negative < 0. Of course, you can always change these thresholds based on the type of text you are dealing with; there are no hard constraints on this.
Unsupervised sentiment analysis models make use of well curated knowledgebases, ontologies, lexicons and databases which have detailed information pertaining to subjective words, phrases including sentiment, mood, polarity, objectivity, subjectivity and so on. A lexicon model typically uses a lexicon, also known as a dictionary or vocabulary of words specifically aligned towards sentiment analysis. Usually these lexicons contain a list of words associated with positive and negative sentiment, polarity (magnitude of negative or positive score), parts of speech (POS) tags, subjectivity classifiers (strong, weak, neutral), mood, modality and so on.
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