3 Ways to Write Reviews for Amazon Get Paid (Not Just Free Products!)

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You can report a seller by clicking on the item you were interested in and then clicking on their name. Once on their profile, click the three dots on the page and choose 'Report'. You should then block them so they are no longer able to contact you.

Now just wait few days. Amazon will go through the video thoroughly and let you know if you have meet all the requirements. When your video gets rejected, they will tell you why, so you can fix it. If you follow the instruction carefully, shouldn't have any problem. Easiest way to create a close caption is to let Youtube generate it for you. If you go to CREATOR STUDIO and go to your video, you'll see SUBTITLE option on top menu. Go there.

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Another study conducted by Jindal and Liu [4] constructed a dataset based on the rule-based method from Amazon. They found that three kinds of repeated reviews appear to be fake reviews: various reviewer IDs on the same product, same reviewer IDs on various products, and various reviewer IDs on various products. The authors used the Jaccard distance method to calculate the three types of repeated reviews concerning the review text's similarity. They considered the review is fake if the similarity is greater than 0.9. This dataset contained 5.8 million reviews, where 55,000 were labelled as fake. Using different methods with a defining set of rules, the authors [89] and [90] constructed datasets contain 6,819 and 2.848 review for book and hotel domain, respectively. Later, Barbado et al. [79] crawled through review datasets based on the web-scraper process from Yelp.com. They labelled them based on content and user behavioural features, where 9653 reviews were labelled as fake and 20828 genuine reviews. These datasets were annotated depends on the rule-based method. The method based on rules does not rely on manual annotations, and the cost of annotations is relatively low. A large number of annotation data is simple to create, but some noise is present. For example, using the dataset constructed by Jindal and Liu [4], they considered the review as fake if different users posted reviews of the same product, or the same users posted reviews of the same product, or different users posted reviews on different products. Considering these review rules as fake is not reliable because there is a phenomenon where the same consumer has a few assessments with a high likelihood for the same product due to the mismanagement or network link, yet they labelled them as fake. So, this annotation approach needs to be discussed. Motivated by this, Khurshid et al. [18] extended their previous work and proposed an ensemble learning model to detect fake reviews based on selected features. The proposed model consisted of two tiers: Tier 1 used three classifiers (Discriminative Multi-nominal Naive Bayes, a library for Support Vector Machine and J48), and Tier 2 used Logistic Regression classifier to introduce an accurate result. They also used the following feature selections to extract structural and linguistic features: Particle swarm optimization used to explore the feature space, Cuckoo Search used to explore the attribute space, Greedy stepwise, carried out in vector space and Chi-Squared utilized to evaluate the worth of an attribute by calculating the value of Chi-Squared statistic value. They evaluated the proposed model on a real-life dataset [8], and a semi-real dataset [77]. The experimental results showed that the chi-squared feature plays a significant role in improving the proposed model's performance with an 84.1% accuracy on the Yelp restaurant dataset and 81.7% semi-real dataset. However, the proposed model performance could be improved by integrating the chi-squared feature into the deep learning model.

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Employees on medical leave are protected from losing their jobs or being retaliated against for taking leave under the Family and Medical Leave Act (FMLA). Amazon offers a variety of leave options, including 2 weeks' paid sick leave and unlimited unpaid time off.

23. Upwork Fancy Hands receives a 3.3-star rating on Indeed

23. Upwork Fancy Hands receives a 3.3-star rating on Indeed

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