Yang et al.YYQB15 solved the problem of helpfulness prediction from a di erentangle by hypothesizing that helpfulness is an internal property of text. They have usedsemantic features like INQUIRER and LIWC in review helpfulness prediction. Theinsight behind is that people usually embed semantic meaning such as emotion andreasoning. A regression model is trained on Amazon dataset and model was validatedusing human annotation. They achieved better Results with very less RMSE whenGALC, LIWC and INQUIRER features are combined. Also, the Cross-category testshows that semantic features can be transferable to other categories.Qazi et al.QSR+16 developed a concept model for helpfulness prediction. This studyconsiders not only the quantitative factors such as a number of concepts also qualitativeaspects of a review including review types such as regular, comparative and suggestivereviews and reviewer helpfulness. The set of 1500 reviews are randomly chosen fromTripAdvisor across multiple hotels for analysis and a set of four hypothesis are used totest the model. Results suggest that number concepts contained in a review, numberof concepts per sentence and the review type contribute to the perceived helpfulness ofonline reviews.Liu et al.LJJ+13 focused on how to automatically evaluate the helpfulness of a reviewfrom a designer’s viewpoint entirely using review content. They have conductedan exploratory study to understand what makes the review helpful from the productdesigner viewpoint.Ghose et al.GI11 analysed the impact of online reviews on economic outcomes likeproduct sales and see how various factors impact the usefulness. their approach exploresthe multiple aspects of review text such as subjectivity, readability and spellingerrors to identify the important text-based features. They also explore the reviewerlevel aspects like average rating. An analysis reveals that extent of subjectivity, informativeness,readability, and linguistic correctness in reviews highly inuence the salesand perceived usefulness. Reviews with the mixture of objective and highly subjectivesentences inuence the usefulness negatively. Using Random Forest based classi ers,they have shown that usefulness can be predicted accurately.Singh et al.SIR+17 proposed a model using ensemble learning into the website itselfto predict the helpfulness of online consumer reviews. The proposed system would beable to perform the initial evaluation of the review. That would help in prioritizing the

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