Manually analyzing this abundance of text produced by customers or potential customers is time-consuming. Less-structured outlets like blogs, Twitter, Facebook, and Instagram can also be useful sources of insight on customer sentiment, as well as feedback on product features and services that inspire praise or condemnation. Sources like Amazon ratings and reviews on TripAdvisor, Google, and Yelp can literally make or break products. In markets like travel, hospitality, and consumer electronics, customer reviews are now considered to be at least as important as evaluations by professional reviewers. Social media has revolutionized the way people make decisions about products and services. Intent analysis determines likelihood to take action.īusinesses can use insights from sentiment analysis to improve their products, fine-tune marketing messages, correct misconceptions, and identify positive influencers. Emotion analysis is a variation that attempts to determine the emotional intensity of a speaker around a topic. Businesses can use this insight to identify shortcomings in products or, conversely, features that generate unexpected enthusiasm. Aspect-based sentiment analysis goes one level deeper to determine which specific features or aspects are generating positive, neutral, or negative emotion. There are several types of sentiment analysis. Irony and sarcasm are also challenging because the speaker may be saying something positive while meaning the opposite. In order to understand the phrase “I like it” the machine must be able to untangle the context to understand what “it” refers to. For example, the exclamation “nothing!” has considerably different meaning depending on whether the speaker is commenting on what she does or doesn’t like about a product. Basic models primarily focus on positive, negative, and neutral classification but may also account for the underlying emotions of the speaker (pleasure, anger, indignation), as well as intentions to buy.Ĭontext adds complexity to sentiment analysis. Sentiment analysis is a subset of natural language processing (NLP) that uses machine learning to analyze and classify the emotional tone of text data.
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