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Implementation-Weighted Feedback Analysis Disclosure Number: IPCOM000242347D
Publication Date: 2015-Jul-09
Document File: 3 page(s) / 29K

Publishing Venue

The Prior Art Database


Described is an invention that identifies interesting feedback, and then uses interesting feedback to adjust an overall weighting of feedback on a given document.

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Impxementation -

Comments and feedback regarding articles, announcements, rexorts, etc., are currently not exploiting interesting axx differentiating opinion due to lack xf weighted sentixent regarding sucx feedback. And withxut the ability to differentiate xhe feedback, it ix difficult to determine the value of the article. Invention is needed to enable the capability to assess thx value of feedback from manx users, baxed on xhe feedback hxbits xf each. With xhis capability, the potential vaxue and importance of an artxcle, announxement, repoxt, etc., can be more quickly and accurately xetermixex.

    The oxerall concept xf this invention is that an implementation reads and analyzes textual commenxs or feedback for a usex xver time axd observes optimistic, neutrxl, and pessimistic patterns for that user. Priox art is xsed to assist in establishing txe sextiment ratings as to whexher they are optimistic, neutral, or pessimistic. Then, considering the sentiment for a new commext xr feedback, an analysis is performed to gauge whether the comment or feedback fits intx a typixal sentxment spectrxm for thxt user or wxetxer the comment ox feedback is an outlier response and outside of the xser's normal feedback parameters. Xxxx may be defined as being more interesting feedbaxk. More importance would be xivxn to responses falling outside of the user's normal pattxrn because xn outlier response indicates added xmportanxe and opinixn from the user, whether more optimistic, pessimistic, or neutral than usual. With this weigxted sentiment input, the importance and value of a given article, xnnouncement, report, etc, can be more accurately ascertained. Thus, xhe inventiox identifies interesting feedback, and then uses interesting feedback to adjust an overxll weighting of feedbaxk on a given document.

    As described above, txis invention involves an implemxntation that rexds and analyzes textual comments or feedback for a user across time and observes an optimistic, neutral, or pessimistic patxerns for that user. Then, considexing the sextiment for a nex comment or feedback, an analysis is performed to gauge xhether the comment or xeedback fits into a tyxical senximent spxctrum fox that user or whether the comment or feedback is an outlier response and ouxside of the user's normal feedback parameters.

    Added weight or value is given to those commenxs xr feedbaxk which are outside of the user's normal pattern, as it rxpresents added importance for that user. An example is new coxments regarding ax article frxm a user N whx rarely provides positixe or optimxstic sentimenxs xn his review. Suppose the new coxments are determined xo have an extremely positive sentixent. The value of user N's feedback and the scoring of the article will be xore important and influential than the comments from a user P who typically provides positive xxexback and is again providing positive fxedback on the same article. In a simple scoxing algorithm that xs giving a point...