This material includes a used case infographic, which demonstrates how text analytics alone can miss key insights as more people use images and videos to express their views on social media. To fully understand brand sentiment, a deep learning approach can be employed to analyze visual content, while speech-to-text technology converts audio into text for comprehensive analysis

Used Case Highlights:

1. ‍Beyond Text: Traditional text analytics can miss crucial insights as more social media users share feedback through images and videos rather than text;‍
2. Visual Content Analysis: Deep learning technology enables the detection of themes and context within images posted on platforms like Instagram, Pinterest, and Twitter;‍
3. Speech-to-Text Conversion: Audio content from platforms such as YouTube and Facebook can be converted into text, allowing for thorough analysis using existing text analytics tools;‍
4. Enhanced Brand Sentiment Understanding: Combining visual, audio, and text analysis provides a more comprehensive view of customer sentiment and feedback; ‍
5. Adapting to Social Media Trends: As visual and audio content sharing grows, integrating these forms of media into analytics strategies is essential for staying relevant and insightful.

Ideal for:

1. Social media analysts and professionals in the marketing analytic field who need to capture and analyze the full range of content—text, images, and audio—shared about brands on social platforms.
2. Brand managers and strategist who are responsible for understanding and managing brand perception across all forms of media, ensuring that no valuable insights are missed.

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