Chances are you’ve heard the concept of sentiment analysis thrown around in association with the topic of natural language processing (NLP), but what exactly is it and how does it work? We break down the answers in this blog post.
Before we can deconstruct sentiment analysis, we need to understand NLP. NLP is a branch of machine learning and AI which deals with human language, and more specifically with bridging the gap between human communication and computer understanding. Sentiment analysis is a core application of NLP that refers to detecting the attitude (positive, negative, neutral) of a piece of text through an automated process.
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A sentiment analysis task is generally modeled as a classification problem, where a classifier is given text and spits out a category such as positive, negative, or neutral. Sentiment analysis can be particularly useful when faced with a lot of text data that would be too time-consuming to manually label. As the name alludes, “sentiments” deal with opinions, attitudes, and emotions versus objective facts. There are two main types of sentiment analysis which are outlined below:
- Subjectivity/objectivity analysis: Here, a sentence or fragment of text is classified into either a subjective or objective category. This can be challenging because a word or phrase’s meaning tends to have a lot to do with its context.
- Feature/aspect-based analysis: Here, different opinions or sentiments can be identified in relation to different aspects of an entity. For example, with a product review, you’ll want to know which specific aspects or features people are mentioning in a positive, negative, or neutral light.
The results, therefore, can be immensely valuable for organizations to understand how customers feel about their brand, product, or service. The technique is predominantly used to mine subjective information across texts, tweets, blogs, news articles, and customer reviews. A common application of sentiment analysis involves combing social media comments to identify and extract opinions from users that organizations can then use to identify areas of opportunity or risk areas in need of optimization.
Sentiment analysis can be a tremendously valuable tool for data-savvy organizations in order to get a front row seat to customer dialogue, make any changes to product specs based on frequent feedback, and get a handle on overall reputation management.