Definition:
Sentiment analysis is a type of natural language processing to track the public’s mood about a particular product.
Sentiment analysis involves building a system for collecting and categorizing opinions about a product. Also known as opinion mining, it can be automated using machine learning, a type of artificial intelligence (AI), to extract sentiment from text.
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Applications of sentiment analysis
With this system, you can help marketers evaluate the success of an advertising campaign or new product launch or analyze whether versions of a product or service are popular.
For example, a review from a website might be quite positive about a digital camera, but at the same time it is specifically negative about how heavy this camera is. Being able to identify this type of information in a systematic way gives the provider a much clearer picture about public opinion than surveys or focus groups, because the data is provided by the customer directly.
How sentiment analysis works
- We analyze the exact intent of a phrase to know if it speaks of a brand or a product.
We value the phrase through polarity, which classifies the message according to the author’s intention, and can be positive, negative or neutral. With this you control the feeling of the users. - Processing of the information of the previous points. This process is divided into:
- Manual: if the word or expression can have different meanings depending on the field, that word will have to be classified manually where appropriate.
- Automatic analysis: different keywords are established that frame the expressions depending on what words or set of similar words you have. An example of this could be that any phrase that carried “good”, “beautiful”, “beautiful” was included in the positive feeling section.
Limitations of Automatic Sentiment Analysis
The main one is that it is not as effective in ranking as manual sentiment analysis can be.
In addition, the programs that are responsible for the automation of these tasks are not able to differentiate exactly if a sentence contains sarcasm or irony and, therefore, could confuse a sarcastic phrase of the style “yes, yes, that product (x) is very good but on the other end”. Therefore, the vast majority of software are not able to know the real intention of a sentence and, consequently, catalog it as positive or negative.
There are certain programs that are able to differentiate a sincere phrase from a more ironic one but they are somewhat more complex to configure.
Frequently asked questions about Sentiment Analysis
What is Sentiment Analysis?
Sentiment Analysis is a technique that classifies opinions, comments or texts according to the attitude they express, normally positive, negative or neutral. It is used to interpret brand perception, user satisfaction and reaction to content or campaigns.
What is Sentiment Analysis used for in marketing?
It is used to detect opinion trends, prioritize incidents, evaluate reputation and understand how a message is received. It can be applied to reviews, social networks, surveys, support tickets or brand mentions in digital media.
What is the difference between Sentiment Analysis and social listening?
Social listening collects and monitors mentions in digital channels. Sentiment Analysis interprets the tone of those mentions to estimate perception. Listening identifies what is said and where; sentiment helps understand how it is valued.
What limitations does Sentiment Analysis have?
It can fail with irony, cultural context, ambiguous language, technical terms or very short texts. That is why it is advisable to review samples manually, adjust models to the sector and not make critical decisions only with an automatic label.