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.
Advancements in Calculating Sentiment Score
User-generated content is qualitative in nature i.e. it can be in the form of comments, videos, images, audio, and so on. This customer feedback now needs to be quantified, as in, we need to assign numeric values to it in order to calculate the sentiment score for the content. In social media listening analytics, metrics can be defined in the form of number of likes, number of user-generated content received by a brand (volume), the general sentiment in the content- whether it’s positive or negative; and of course the number of shares the content received (virality).
As data scientists explore new ways to get sentiment scores that are more precise and accurate, there have been vast advancements in calculating sentiment scores. This is a very important step ahead for marketers and professionals in many fields such as finance and banking. This has affected the way we calculate social sentiment scores as well.
- Traditional method
The traditional way of assessing the effectiveness of a brand’s marketing strategy is to assess it by quantifying consumer actions such as the number of times a video is viewed, or the number of shares, likes, etc. It is assumed that these numerical metrics about ad-spend and the number of shares or posts, when compared to each other, indicate the effectiveness of a marketing campaign.
Limitations – Unfortunately, this is a flawed thought process. Simply because, if a comment or a review video is posted among engagement groups on a social channel or if it a paid content, the platform’s algorithm will consider the content as high quality and thus promote it and so increase its visibility. In such a case, the social platform’s algorithm causes the video to have very high click rates simply because it was posted in a “group” or because it was paid for by a brand.
If as a marketer, you take this method of sentiment scoring for your content as positive and invest further in similar campaigns, you are walking into completely unpredictable territory and a risky investment of time and resources. As you may have realized, by this logic, irrelevant and inaccurate content can also be inferred as popular and relevant by the algorithm just because of its click-through metric. This is detrimental to deriving an accurate social sentiment score.
- Content-based sentiment score
To be able to truly understand the effectiveness of an advert campaign or user-generated content for your brand on social platforms, you need to calculate its sentiment score through social media sentiment analysis. If it is surveys or emails, you will need text analysis, and if it’s videos, then a video content analysis platform will fulfill this requirement. But in any case, there is a vital need to assign a numerical emotion score to the content itself.
Below are two reviews in the hospitality industry by guests of the same hotel.
Customer A writes, “The hotel was great, the staff was friendly and room prices were quite reasonable. I surely recommend them! ★★★★★ (5/5 stars)
Customer B writes, “My room was cold and we had to wait for hours for the hotel staff to adjust the thermostat, even though the hotel seemed empty. When we tried to call the reception to enquire, they seemed impatient and rude. ★☆☆☆☆ (1/5 stars)
Content analysis to figure how to calculate sentiment scores is done through multiple approaches. Let’s see them in detail.
1. Star Rating-based sentiment score
The most simple way to assign a positive or negative score to the content is by star rating. But in this case, when you take millions of reviews, you cannot really know why a brand received a particular rating. This might not be very useful to a discerning customer.
2. Text-based sentiment score
The simplest analysis of text for emotion mining will be to train an AI-based natural language processing platform to assign scores to the content based on keywords. In the case of the 1st review, it will be because of key words such as, “great, friendly, surely recommend”. And as for the 2nd review, the algorithm will mark it as negative based on the words “impatient” and “rude”. Since the ratio of positive and negative reviews is 1:1, the aggregate sentiment score of the hotel will be neutral as they cancel each other out (50% positive−50% negative=0).
However, this approach too, does not calculate the context of positive and negative sentiment to the reason behind why a brand received a particular score. There is no semantic consideration either.
3. Aspect-based sentiment score
For a more detailed and accurate analysis of sentiment to arrive at a precise sentiment score, the content itself needs to be broken down into smaller pieces for scrutiny by using the aspect-based sentiment analysis. For example, in the 1st review, customer A talks about the staff and the price, while in the 2nd review, customer B is talking about the rooms and the staff. He does not mention the price at all. In both these cases, the algorithm will consider the aspects of price, room, and staff, while calculating the aggregated sentiment score.
Here too, machine learning platforms with a sentiment analysis API need to have a semantic understanding of the content, not just lexical, to calculate the aggregate social sentiment score. This is important because in many cases, a post needs to be understood in the context it was written in. That’s why companies, many a time, ask reviewers to post photos or ask them to assign star ratings. All these different methods when conjoined, offer a more efficient way of calculating sentiment scores.
