Sentiment analysis, also known as opinion mining, is the process of analyzing digital text with AI to determine the emotional tone or attitude of the author. This has numerous different use cases in Sales and Marketing, social media research, brand health, crisis management, and e-Commerce recommendations.
Sentiment analysis is done with natural language processing (NLP) techniques. The first step is preprocessing the text. Preprocessing involves:
- Tokenization: Where the AI assigns a numerical value to every unique word of the text.
- Stemming: The process of reducing words to their base form. For example, running, runs, and ran would all be converted to run. This ties into tokenization because all of these words would be given the same value and considered to be the same by the AI.
- Stop-word removal: Stop-words are words like at, of, and the that don’t have meaning. These words are important to remove to reduce the cluttering in the text, making it easier for the AI to understand.
After preprocessing, it’s much easier for the AI to extract meaning from the text, but what it does with that information depends on the type of sentiment analysis being employed.
So, what are the different types of sentiment analysis, and how do companies use them?
Graded analysis revolves around determining the overall feeling about something using a rating scale classified by the categories: very positive, positive, neutral, negative, and very negative. The insight provided by graded analysis is pretty surface-level, so it’s typically used with fine-grained and aspect-based analyses, which take a closer look at each part of the sentence to identify the sentiment of each subject. For example, in the sentence “The keyboard is too loud for my preference,” these analyses would determine that the main subject is the keyboard, and the sentiment associated with that is negative. When a company asks you to complete a survey about their product, they’ll likely use all of these analyses to parse through your response, it’s much cheaper and time efficient than reading answers manually. However, graded, fine-grained, and aspect-based analyses are mainly for analyzing reviews after someone already bought the product. Companies use intent analysis to determine if a person is likely to become a customer so they can target them with advertisements. This is often done through social media, and many social media companies have data privacy terms that state that they share messages, comments, and posts on their platform with advertisers for this purpose.
There are a few drawbacks to implementing sentiment analysis as a company, though. For one, the technology tends to struggle with negation, especially across two or more sentences. Take this example, “I thought the cable would be the right length. It wasn’t.” AI would have a hard time connecting “It” to “the cable” and incorrectly label this as positive. Sarcasm is another weak point of sentiment analysis. The sentence: “The product came completely broken, how wonderful” could be incorrectly classified as very positive. Relying solely on sentiment analysis could give companies a false impression of customer satisfaction.
Despite its imperfections, sentiment analysis techniques are extremely beneficial for companies and save them both time and money.
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