How To Calculate Sentiment Scores
Overview A sentiment score helps in measuring the emotions reflected in a piece of text. Quantifying the sentiment in customer reviews or feedback in terms of positive and negative polarity helps us learn how customers feel. Brands use this information for business intelligence and see patterns in data across different functions of the business directly related to a customer to make data-backed growth decisions. We have created this easy-to-understand layman’s guide showing how to calculate sentiment scores so that brands like you can have a broad view of what goes on under the hood of a sentiment analysis engine.
What is Customer Sentiment Analysis? Customer sentiment analysis is the processing of customer reviews and feedback for emotion mining to know exactly what the customer thinks about a brand or a particular product or service. Data for this kind of sentiment analysis can be gained from several places like voice of the customer sources like surveys, call centre logs, video interviews, user-generated videos, product reviews, and the like. AI-based sentiment analysis platforms use many machine learning tasks like natural language processing (NLP) and named entity recognition (NER) to give an in-depth analysis of customer sentiments. NLP processes the text (for videos, audio is first transcribed into text) by part-of-speech tagging, lemmatization, calculating prior polarity, negations, and semantic clustering. After this, sentiment scores are assigned to each of the extracted topics, with scores ranging from -1 (true negative) to 1 (true positive) to the text. A neutral score means no sentiment or emotion was expressed. This is then presented on a sentiment analysis dashboard.
Calculating the Sentiment Score 1. Word count method Based on the opinion lexicon we used in the above example, a simple way to calculate the sentiment score would be to reduce the negative from the positive occurrences as below. 1 - 2 = -1 Thus, the sentiment score of the above example is -1. This is a simplistic way of calculating the sentiment score, however, for longer, more complex sentences, we would use a different method.
2. Deducing sentiment score with the length of the sentence We subtract the number of positive words from the negative words and divide the result by the total number of words in the review sentence. 1-2/42 = -0.0238095 Although this system is better for longer reviews, the resulting score can be very minute and so difficult to differentiate when done at scale. Alternatively, many data scientists choose to multiply scores by a singular digit to get larger scores that make comparison easier.
3. Ratio of +ve and -ve words counts In this method, we divide the total number of positive words by the total number of negative words and add it by one. 1/ 2+1 = 0.33333 In this method, the ratio normalizes the total length of the text a bit. This is good because the longer the review is, the more the count of positive and negative scores is. In this method, a score around 1 is set as neutral. Data scientists consider this method of calculating sentiment scores to be more balanced.
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