Sentiment analysis is the science of inferring insights from data generated in social media and goes beyond social media metrics such as the number of likes, shares, mentions, and followers. Sentiment analysis is a significant component of social media analytics; it involves processing consumers’ opinions, conversations, and sentences to detect and understand consumer emotions and mindsets toward brands or topics. The data gathered might be from blogs, forums, social media posts, reviews, wikis, and more. Sentiment analysis uses parameters such as context, tone, and emotions among others.
Sentiment analysis categorizes the divergence (positive or negative) of a post, comment, or statement that establishes the positivity, negativity, or neutrality of the sentiments around a topic.
Social media monitoring software is one of the ways companies use sentiment analysis. These software packages and tools use natural language processing techniques and statistics to analyze sentiments on huge amounts of social data.
Analyzing audience sentiment in online conversations is a leading indicator in foreseeing the outcomes of coordinated activities as varied as political events, marketing campaigns, and product launches. For example, sentiment reports pulled in advance of a presidential election can help identify trends that can predict the winner.
Business questions that can help gauge sentiment include the following:
- How do people feel about the product or brand?
- How are people responding to campaigns or product launches?
- Is there a way to predict the outcome of a campaign or event so that the company knows how to invest in marketing?
- Why aren’t consumers buying the product?
Sentiment analysis of social data empowers business decision-makers to understand consumer attitudes and behaviors. Since social data is voluntary and user generated, mining this data and effectively classifying or coding it allows companies to forecast with some certainty customers’ feelings towards campaigns, brands, content, and products.
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