We used Sentiment Evaluation to get extra insights into buyer suggestions

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It was found that the purchasers have been beneficiant in offering larger satisfaction scores (starting from 1-5). Nevertheless, there have been situations the place suggestions within the free textual content subject was not as optimistic. This was a possible space that could possibly be addressed by the group to enhance buyer satisfaction and thereby forestall surprising income erosion.

We used ‘Sentiment Evaluation’ to get extra insights into buyer suggestions aside from the score offered. For coaching information, we needed to create a coaching set manually based mostly on the suggestions that was traditionally offered by the shopper and categorized as “Constructive or Impartial” or “Detrimental.” We used tokenizer & glove embeddings for information pre-processing and skilled a Bidirectional LSTM mannequin utilizing Adam optimizer. We chosen this mannequin as we discovered that this carried out comparatively higher with lesser complexity. The mannequin was used to foretell if the suggestions was “Constructive or Impartial” or “Detrimental.”

The problem of constructing this mannequin was primarily to manually create giant coaching information set based mostly on the historic information out there. Additionally, we needed to do information clean-up earlier than pre-processing to take away particular characters and typos. We additionally needed to fine-tune the coaching set, and its quantity as preliminary outcomes on the validation information set was not very passable.

Based mostly on the suggestions class prediction by the mannequin, further engagements have been initiated with the highest prospects. We predict this initiative to enhance our share of pockets with these prospects.

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