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Using different techniques the paper aims to create an approach that can be adopted by companies to develop automated systems for sorting and classifying large amounts of OCR. Sounds exciting, doesnt it?!Lets have a look at how this works. Whereas previous literature focusses at the factors that determine the perceived helpfulness of a review, this paper takes a step back. It starts by considering the factors that determine the likelihood of a consumer paying attention to a review in the first place, since without reading a review you cannot determine its helpfulness. Hence the research questions are as follows:Research Question 1: Which factors determine the likelihood of a consumer paying attention to a review?Research Question 2: Which factors determine the perceived helpfulness of a review?In order to answer the research questions, the paper looks at a sample of 2616 Amazon reviews and considers several factors they believe may impact review readership, helpfulness, or both. Readership is measured as the total number of votes helpful and not helpful, whereas helpfulness is measured as the proportion of helpful votes out of total votes. Since I see no reason to bore you with detailed methodologies, I made a quick and easy to follow overview of the different factors the paper considers using a random Amazon review as an example:All above outlined findings are statistically significant. Whereas previous research focussed mainly on numerical rating and length of the review, this paper looks at the textual information the review contained. This means that the practical implementations are high. For example, the paper suggests that companies may use sentiment data to analyse large amounts of OCR which are constantly produced on the Internet. The paper also showed the importance of the title: make it short and not too emotional.



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