Measuring & Understanding Sentiment Analysis Score

Sentiment analysis is used to understand the emotion behind verbatim comments from open-ended questionnaires. There are many ways to apply sentiment analysis in your business, from market research to employee surveys. Measuring and understanding sentiment analysis scores is more involved than analyzing your closed questions, but offers an important source of valuable information.

 

What Does Sentiment Mean?

Your audience will have opinions that are either positive, negative, or neutral in tone. You can use this information to better understand the feedback given, as it’s considered in the context of a verbatim comment. Sentiment analysis provides your business with a way to quantify these emotions to discover insights into your company, products, and services.

Because verbatim comments are provided in the person’s own voice, and not based on a set response or keywords, you need a way for your computers to understand it. Natural Language Processing, combined with machine learning, allows your sentiment analysis solution to look at a data set and pull more meaning from it. It does this by scoring each response based on whether the algorithm thinks that it’s positive, negative, or neutral.

 

Analyzing versus Interpreting

While analysis and interpretation are often used interchangeably, they have two different meanings. Interpreting sentiment in a series of responses is more of a qualitative assessment. If you are manually processing verbatim comments to determine the sentiment, you are most likely interpreting the results due to the subjective nature of this process and the potential for biases to be introduced.

Analysis has a standardized approach that gets the same results regardless of the person running the process. It’s difficult to achieve this manually, but computer-aided methods make it possible.

 

Positive Sentiment Words

These words are representative of positive sentiment, although looking at the whole context surrounding the comment is important. You’re able to find out what people like about your company, products, and services, and the highlights of their experiences. This is a good way to see what you’re doing right and areas where you compare favorably to the competition. You can build on these successes as you move forward as a company.

  • Acclaim
  • Accolade
  • Adaptable
  • Affordable
  • Brilliant
  • Convenient
  • Durable
  • Enjoyable
  • Ethical
  • Faultless
  • Generous
  • Helpful
  • Improved
  • Joyous
  • Long-lasting
  • Marvel
  • Nice
  • Outperforming
  • Pleasingly
  • Refined
  • Satisfied
  • State-of-the-art
  • Terrific
  • Unparalleled
  • Vibrant
  • Well-rounded
  • Wholesome

 

Negative Sentiment Words

Here are words that are commonly associated with negative sentiment. These sentiments can indicate areas where you’re failing to deliver on expectations. It’s also a good way to see whether a product or service has a widespread problem during a rollout, to identify issues in the customer experience, and to find other areas of improvement that you can prioritize.

  • Two-faced
  • Abrasive
  • Annoying
  • Backward
  • Careless
  • Debacle
  • Dishonest
  • Failure
  • Gruesome
  • Hazardous
  • Imbalance
  • Lackadaisical
  • Limited
  • Manipulative
  • Nasty
  • Disgusting
  • Offensive
  • Painful
  • Regret
  • Risky
  • Scam
  • Terrible
  • Ugly
  • Unauthentic
  • Woeful

 

Neutral Sentiment Words

Neutral sentiments are driven by context, so it’s important to look at the whole comment. Excelling in the customer experience means going beyond “okay” and moving in a positive direction. These middle of the road sentiments are useful in determining whether your company is noteworthy in a product or service category.

 

Ratio of Positive to Negative Comments

Ratio in sentiment analysis is a score that looks at how negative and positive comments are represented. Generally, this is represented on a scale of -1 to 1, with the low end of the scale indicating negative responses and the high end of the scale indicating positive responses. You may need to adjust how you evaluate the score to account for trends in your audience, as some may be more negative than the standard population. For example, if you were conducting a survey that focused on dissatisfied customers, then you would be dealing with a tone that’s more negative than usual.

 

What is a Good Sentiment Score?

A good sentiment score depends on the scoring model that you’re using. Set minimum scores for your positive and negative threshold so you have a scoring system that works best for your use case.

 

How Accurate is Sentiment Analysis?

The accuracy of sentiment analysis depends on the method that you’re using to work with your verbatim comments, the quality of the data that you’ve given the computer, and the subjectivity of the sentiment. You want the most accurate results possible, which typically means that you’ll want to have a computer assisting your researchers with this process. The automated system can reduce the potential for bias and also use a standardized set of rules for going through the data. If there are any problems with accuracy, you can feed more data into the sentiment analysis solution to help it learn what you’re looking for.

 

What Algorithm is Best for Sentiment Analysis?

The algorithm that works best for sentiment analysis depends on your resources and your business needs. There are three major categories in algorithms: machine learning, lexicon-based, and those that combine both machine learning and lexicons.

Machine learning is one of the most popular, as it allows your sentiment analysis solution to keep up with changing language. You never know when the next shift in slang and voice will come around and completely change what is negative and what is positive. Machine learning uses the data that you provide it to understand natural language and current vernacular. This is an example of supervised learning, where you’re feeding it representative results so it can learn from it. Unsupervised learning refers to machine learning that is not based on data specifically designed to train it.

A lexicon-based algorithm relies on a list of words and phrases and whether they’re positive or negative. It’s difficult to update the lexicon with the latest trends in language.

If you are looking to uncover insights from verbatim comments with ease, check out Ascribe’s fourth generation text analytics offering, CX Inspector. CX Inspector, is a customizable and feature-rich text analytics tool that provides topic and sentiment analysis from verbatim comments automatically.

For a more comprehensive solution add X-Score, a customer measurement approach that provides a customer satisfaction score from open-ended comments.

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