Sentiment Analysis is Key for Social Listening and Social Media Monitoring

There are two terms, social media monitoring and social listening, which are frequently used and are largely synonymous. They look at blogs, review sites, and anywhere your company is being talked about.  They both focus on direct communication between you and others, but also cast their net farther to any place on the Web where people are discussing your company or products.

Sentiment Analysis is a critical technology that enables both social media monitoring and social listening.  It automatically performs monitoring by detecting likes, dislikes, and emotions found in many sources, including, say, social media platforms, independent blogs, and forum posts. Sentiment Analysis also provides the ability to aggregate and analyze this data.  Here are specific ways in which Sentiment Analysis helps companies stay in close touch with their customers:

1. Tracking the Volume of Mentions. At a very basic level, Sentiment Analysis allows a company to track over time the number of mentions of their brands, products, or slogans. If there’s a sudden spike, Sentiment Analysis can send an alert. You would think a high level of buzz would always be good (at least according to the age old cynical adage, “There’s no such thing as bad publicity”), but it could also obviously be bad, as when an ad becomes unintentionally controversial.
2. I love it/I hate it/Meh. Sentiment Analysis also detects positive and negative sentiment. At a very basic level, it identifies positive and negative language over a whole document and gives a single quantitative rating. Obviously, this isn’t optimal, as there can be multiple sentiments in one document (even something as short as a tweet: “I loved Game of Thrones, but the last episode was disappointing”). At an advanced level, known as Entity- and Aspect-based Sentiment Analysis, it pinpoints the specific aspects that those positive and negative sentiments are about. For instance, customers may love the latest store-wide sales, but be greatly disappointed by long lines, messy shelves, or less than wonderful customer service.
3. Ability to Handle Big Data. Advanced Sentiment Analysis can scale to Big Data size. This allows a company to process large amounts of sentiment data in real time. Processing large amounts of data quickly causes the Sentiment Analysis results to be very fresh and relevant.
4. Enabling Analytics. Advanced Sentiment Analysis also normalizes the extracted data. All statements expressing the same sentiment (e.g., “I hate X” vs. “I really dislike X”) will receive the same representation in the output. This allows the sentiment data to be aggregated over very large collections of content. A popular visual display of this aggregated data is in the form of a dashboard where the sentiment data can be sliced and diced, for instance, by its positive and negative aspects. If needed, the user can drill down to the source text in order to get confirmation of a sentiment’s accuracy. In addition, by using location metadata and automated geocoding of mentioned place entities, sentiment can be plotted on a map to predict, for instance, where there will be higher demand for which products.

In summary, in today’s interconnected world where commerce is more and more conducted over the internet, companies need tools that will help them get a clear and accurate view of how their products are doing in the eyes of the customers. Sentiment Analysis is an AI-based technology that will help. It helps companies perform more cost-effective and accurate social media monitoring and social listening.