The media update
team delves into the topic of sentiment analysis to find out how media analysts are using it and the ways in which artificial intelligence (AI) is changing this field.
1. It gauges positive and negative consumer attitudes
Sentiment analysis studies opinions, attitudes, views, and emotions that people express in text and, in some cases, images. Analysing the sentiment of text – whether it is in the form of an email, newspaper article, or social media post – can reveal the attitude of its writer towards the topic they are addressing.
Also called opinion mining, sentiment analysis can be used by social scientists to understand how people react to events or developments. Individuals like artists, celebrities, and authors also use sentiment analysis to measure public perception towards them or their work.
The good news for marketers is that brands can use it to find insights within media coverage and social media posts to help them improve their offering and enhance their marketing efforts.
2. Sentiment analysis unlocks insights from social media posts
Consumers generate thousands of social media posts every second, some of which mention brands. Social media platforms offer a space where users feel comfortable to voice their opinions, which means their posts are loaded with sentiment.
Analysing the sentiment of these posts can help companies extract value from the massive amounts of data, which social media users create. Social media tracking services, like amaSocial
, offer tools that marketers and PR professionals use to monitor social media posts that mention their brand, as well as set up alerts to be notified,
when posts with a specified sentiment are published.
Social media sentiment reports can help brands to measure whether their campaigns and events have a positive impact on their target market. A look at the posts that have negative sentiment can help these companies improve future campaigns, or change their products based on relevant consumer feedback.
3. This form of analysis can reveal deeper insights with the help of AI
Various methods exist for analysing sentiment. One of the latest methods relies on AI technology to automatically analyse the sentiment of posts. A combination of two AI components – natural language processing and machine learning – is helping brand tracking companies like Newsclip
provide this instant sentiment analysis at near-100% accuracy.
Machine learning is especially useful in sentiment analysis systems that process social media posts. It allows computers to learn from new data, which means that these systems can learn the new slang and shorthand terms that social media users constantly come up with.
To learn more about the role of AI in processing media coverage, read our article How AI makes sense of the media’s Big Data
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