How does sentiment analysis help in my video research?

With today’s end-to-end video research tools,  you don’t need to spend hours searching for answers or painstakingly noting down time-codes to understand sentiment.

Instead, advanced, automated analytics such as theme coding and sentiment analysis empower researchers like you to automate the process. This allows you to quickly understand shared sentiments and consistent themes across hundreds of videos and hours of content. Where’s the easy button?

What does that mean for you? You can make informed, customer-centric decisions faster.

Read next: Getting leadership support — and keeping it — for internal market research

What is automated sentiment analysis?

Sentiment analysis is a video analytics tool available within Voxpopme that is designed to bring you closer to your customers’ feelings towards your products, services and ads. It works by reviewing every sentence of each video uploaded. It determines whether it is positive, negative or neutral and giving it an associated score. With this you can understand the sentiment behind every single sentence, in every single video response and understand the subtle nuances of every comment.

It auto-categorizes content by respondents’ feelings and allows you to effortlessly explore sentiment across wider themes. It’s the ideal blend of human and machine analysis, removing the margin for human error in the categorization stage but still allowing you to pull out the video snippets that are most informative within a category. Essentially, it means you can build an understanding of sentiment without the human biases that are often present in manual analysis, while at the same time vastly increasing speed, scalability, and accuracy. Basically, it allows you to spend less time searching and more time telling stories that resonate – bringing you closer to what your customers think than ever before.

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How sentiment analysis works

Automated analytics can be added to video of any length using an end-to-end video insight platform like Voxpopme. That can include:

  • short form content recorded by consumers
  • existing long-form content (such as focus groups or IDIs)

IBM Watson powers the sentiment analysis and uses machine learning and natural language processing to identify the underlying sentiment.

Sentiment analysis processes the transcribed text and determines whether a sentence is positive, negative or neutral. It then scores each sentence within the transcript between -1 (negative) and +1 (positive) with 95% accuracy.

Sentence-level sentiment means you can identify how respondents answer each of your questions. You can associate attitudes with every sentence for a thorough understanding.

You can view the percentage breakdown of sentiment via interactive bar charts, showing the percentage of positive, neutral and negative responses for a quick and easy overview of the sentiment breakdown:Sentence Level Sentiment Analysis

Automated theme coding demonstrates the sentiment breakdown of each theme discussed within your videos. The sentiment chart for each theme will give you an instant indication of how respondents are discussing each theme, as well as looking at the percentage breakdown of sentiment for each theme.

Sentiment analysis helps you go from:

“63 percent of  respondents mentioned quality”

to

“63 percent of respondents spoke positively when talking about the quality of our brand”Sentiment analysis overview

The benefits of sentiment analysis

Categorizing content by sentiment can build a deeper understanding of large volumes of video. And in less time. That helps you stablish usable insights.

And you can do all this without having to trawl through endless video responses or surveys to do so. Just think of the amount of manual analysis time saved!

Read next: Why you need centralized data to help your brand be more customer-centric

Next, you can easily create powerful showreels telling customer-centric stories.

To sum up, sentiment analysis means you can determine exactly how your customers answer your questions by associating attitudes with every single sentence of their responses. By creating a sortable and searchable picture of how your customers answer each question, you can get to the bottom of what they really think so you can quickly find the insights you need to deliver a much bigger, clearer picture and make informed decisions – fast.

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