Sentiment analysis, sometimes referred to as opinion mining, is a natural language processing (NLP) approach used to identify the emotional tone of a body of text. Organizations use it to gain insight into customer opinions, customer experience and brand reputation. Businesses also use it internally to understand worker attitudes, in which case it is generally called employee sentiment analysis.
Sentiment analysis works using several steps. First, data is collected and cleaned using data mining, machine learning, AI and computational linguistics. These tools sift through and analyze online sources such as surveys, news articles, tweets and blog posts.
Once sources are processed, features that help the algorithm determine positive or negative sentiment are extracted. Finally, the text is scored using a machine learning model. Positive and negative responses are assigned scores of positive or negative 1, respectively, while neutral responses are assigned a score of 0.
Sentiment analysis categories
Once the sentiment is classified, the analysis can be done. This is divided into four categories:
- Fine-grained analysis, which breaks down sentiment indicators into more precise categories such as very positive or very negative.
- Emotion detection analysis, which identifies specific emotions such as happiness or frustration.
- Intent-based analysis, which identifies what the customer wants to happen as a result of their comment.
- Aspect-based analysis, which examines the specific component being positively or negatively mentioned.
For example, emotion analysis might detect frustration in a product review, while aspect-based analysis might conclude that the negativity is directed toward the product's battery life specifically, rather than the product as a whole.
Despite the benefits of sentiment analysis, there are still challenges to consider. For one, sentiment analysis works best on large sets of data, so it might not offer as much value when dealing with smaller data sets. It's also a new and developing technology that cannot guarantee perfect results, especially given the complicated, subjective nature of human expression. Double-checking results is crucial in sentiment analysis, and occasionally, you might need to manually correct errors.
Overall, sentiment analysis provides businesses with more accurate and actionable customer analytics by gathering and evaluating customer opinions.