Search YouTube Content with Podzinger

Podzinger, the speech-to-text video and audio search engine, has announced the extension of their technology to searching the content in YouTube videos. A click on a tab on the front page of the Podzinger search interface lets the user search for spoken content in YouTube videos. Now, searchers no longer need to rely on just the text and tags provided by YouTube submitters, but can find keyword mentions directly in the video. As noted in the Podzinger blog:

“with this new PodZinger feature, you can access and search YouTube material allowing for more specific search of their user generated content. Now besides simply searching on the metadata of the video files, you can search for terms that are actually mentioned inside the audio, allowing for a greater likelihood you will find relevant material. We’re also automatically organizing the videos into channels based on the actual content of the video.”

The current channels include entertainment, sports, and anime. More are expected soon.

As the SplashCast blog notes this new feature has potential for those who want to do competitive intelligence. Users tracking mentions of their organization can build an RSS feed on Podzinger based on their search terms and receive alerts from Podzinger that show them exactly the time-stamped location where their term appears in the video and a content snippet whereby they can readily track how their organization is being portrayed in the video.

A series of searches shows that when a video relies heavily on imagery instead of spoken text, Podzinger’s technology has nothing to search and will not deliver the video in its results. On the other hand, YouTube which uses the meta data submitted by the user, will deliver the video in the search results. In short the results will differ for the same term in YouTube and Podzinger. SplashCast recommends that competitive intelligence searchers use two similar feeds, one from Podzinger and another from YouTube. This approach will capture relevant mentions in the spoken text and the meta information.

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