Sentiment analysis is the analyzing textual data such as social media posts or news articles to determine how positive or negative opinions are expressed. It is a means of tracking public opinion and identifying important societal trends, having applications in advertising, marketing, public relations, journalism, and political polling.
The use of analysis for poling the public’s opinion has become especially prominent during recent decades. It is mostly because of the abundance of data that is available online.
How Does It Work?
Tools often used for this analysis include machine learning techniques, such as Natural Language Processing (NLP), and some use a combination of tools. Text is passed through these tools to extract entities, sentiments, and themes. The sentiment of a text is determined by looking at the words used to describe the entities and themes.
There are many different ways to extract data from a text, as many different algorithms are available. Text extraction is often performed using natural languages processing techniques, such as part-of-speech tagging and word sense disambiguation.
Themes can then be determined by looking at the examples of each theme expressed in the text.
Key Sentiment Analysis Concepts
An entity is a word or phrase that refers to a person, place, organization, or other items mentioned in the text. For example, Alice, Walmart, and Time magazine are entities.
Keeping these concepts in mind while performing sentiment analysis will help you get better accuracy.
A theme is a word or phrase that suggests the opinion or attitude of the entity. For example, “productivity”, “education”, “environment”, and “marriage”.
Themes can be positive, negative, or neutral. Themes also often communicate emotions.
The sentiment is a positive, negative, or neutral opinion that expresses an emotion. For example, “positive”, “negative”, and “neutral”.
Sentiment can be determined by looking at the entities mentioned in the text and then looking at the themes for each entity.
The first step to performing this analysis is determining which entities are mentioned in a text. If an algorithm can identify all or most of the entities on a list provided beforehand, it is said to have good “recall.”
The second step is determining how many times a theme occurs for each entity. If an algorithm can capture all or most of the occurrences of a theme, it is said to have good “precision.”
After an algorithm has been able to determine mentions and themes, it then identifies how many times a theme occurred for each entity. If there are any cases where the theme did not correspond to the correct entity on the list, this incident is called “confusion.”
Benefits Of Sentiment Analysis On Data Companies
Data companies that use sentiment analysis to analyze the content of blogs and social media posts can determine how people are feeling about their products, services, or brands. Data companies use this information to make strategic decisions.
A company can use this information to decide whether or not to launch a new product or enhance an existing one, for example.
Social media advertising is another area where this analysis is applied. If a brand has a large social media following, it could use this analysis to determine what kind of content will resonate with followers. Advertisers can then target those followers with content that they feel will be highly influential to these people.
Furthermore, this analysis allows companies to determine which customers are least happy with a particular product or service. For example, it can be useful for companies that want to determine which areas need to be focused on for improvement.
For example, a company wants to determine which region of the world it should focus its advertising budget on. One way to do this is by looking at the sentiment of social media posts from these regions and seeing if there is an imbalance in positive instead of negative comments.