Editor’s note: This post was originally published on Ascribe in October 2020 and has been updated to reflect the latest data.
Sentiment analysis (or opinion mining) is used to understand the emotion or sentiment behind comments and text, allowing data analysts to gain actionable insights from verbatim comments. While measuring and understanding sentiment analysis scores is more involved than analyzing closed questions, it offers a valuable source of metric data.
What Does Sentiment Mean?
Audiences will have opinions on products, services, and more, that are either positive, negative, or neutral in tone. Companies can use this information to better understand the feedback given by audiences on products or how effective or ineffective messaging has been. Sentiment analysis provides your business with a way to quantify these emotions to discover the overall answer polarity and insights into your customer feedback.
Because customer sentiment is provided in the person’s voice, and not based on a set response or keywords, you need a way for your computers to understand it. Natural Language Processing (NLP), 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.
While applying a sentiment score to the entire response can be useful, it does have problems. For example, would you say that this comment is positive or negative?
The food was great but the service was awful.
More sophisticated sentiment analysis can apply sentiment scores to sections of a response:
Food-great: positive
Service-awful: negative
Analyzing versus Interpreting
While analysis and interpretation are often used interchangeably, they have two different meanings, especially within data science and sentiment analysis work. Interpreting sentiment in a series of responses is more of a qualitative assessment. If you are manually processing verbatim comments to determine the sentiment, your overall sentiment results could contain unique biases and possible errors. With sentiment analysis tools, this bias potential and possible interpretation errors are severely diminished in favor of a faster, automated, analysis program.
Sentiment analysis programs have 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 and Negative Keywords
Positive Words
These text analysis words are representative of positive sentiment. However, despite lists like this existing, these words are subject to change and machine learning models are incredibly sensitive to context. This makes the framing of the comment incredibly important. With these machine learning models, however, companies are able to find out what people like about products, and services while highlighting 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.
Here are some of these words:
- Acclaim
- Brilliant
- Convenient
- Durable
- Enjoyable
- Ethical
Negative Words
These words 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.
Here are some of these words:
- Dishonest
- Failure
- Gruesome
- Hazardous
- Imbalance
- Lackadaisical
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.
Positive to Negative Comment Ratio
A ratio in sentiment analysis is a score that looks at how negative sentiment comments and positive sentiment 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 algorithms and lexicons.
Machine learning is one of the most popular Algorithms in both Data Science and Text Analytics and is an application of artificial intelligence. It allows your sentiment analysis solution to keep up with changing language in real-time. Because data scientists can’t predict when the next shift in colloquial slang and voice will occur and completely change what is negative and what is positive. They’ve begun to use machine learning with operational data provided to understand natural language and current vernacular. This is a core component of sentiment analysis and is an example of supervised learning, where you’re feeding it representative results so it can learn from them. Unsupervised learning refers to machine learning that is not based on data specifically designed to train it. Deep learning refers to the complexity of machine learning, with this moniker usually referring to complex neural networks.
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.
Ascribe’s dedication to Sentiment Analysis
If you are looking to leverage sentiment analysis when analyzing verbatim comments and open-ended text responses to uncover insights and empower decision-making, check out Ascribe’s text analytics offering, CX Inspector.
CX Inspector is a customizable and interactive text analytics tool with compatible APIs and unique machine learning techniques that provide topic and sentiment analysis from verbatim comments automatically. Analyze everything from the success of marketing campaigns, product feedback results, product reviews, social media platform comments, and more.
For a more comprehensive solution for sentiment analysis, use X-Score which is a feature within CX Inspector that provides a sentiment score from open-ended comments. X-Score is a great measure of customer satisfaction, and also identifies the largest drivers of positive and negative sentiment.
