Fake Product Review Monitoring & Removal and Sentiment Analysis of Genuine Reviews

Any E-Commerce website gets bad reputation if they sell a product which has bad review, the user blames the e- Commerce website rather than manufacturers most of the times. In some review sites some great audits are included by the item organization individuals itself so as to make so as to deliver false positive item reviews. To eliminate these type of fake product review, we will create a system that finds out the fake reviews and eliminates all the fake reviews by using machine learning. We also remove the reviews that are flood by a marketing agency in order to boost up the ratings of a particular product .Finally Sentiment analysis is done for the genuine reviews to classify them into positive and negative. We will use Bag-of-words to label individual words according to their sentiment

Detecting Spam Review through Spammer’s Behavior Analysis

Online reviews about the purchase of a product or services provided have become the main source of user opinions. To gain profit or fame usually spam reviews are written to promote or demote some target products or services. This practice is known as review spamming. In the last few years, different methods have been suggested to solve the problem of review spamming but there is still a need to introduce new spam review detection method to improve accuracy results. In this work, researchers have studied six different spammer behavioral features and analyzed the proposed spam review detection method using weight method. An experimental evaluation was conducted on a benchmark dataset and achieved 84.5% accuracy

Not only Online Review but also its Helpfulness is Manipulated: Evidence from Peer to Peer Lending Forum

Online reviews have become proposed as useful information for consumers to make decision. Meanwhile, review manipulation will weaken the credibility of online reviews. Except manipulating the review text and rating, we propose that review helpfulness, an important signal for consumer to filter the reviews, could also be manipulated. This study thus explores the existence of review helpfulness manipulation and the relationship between firm quality and review manipulation. Based on a dataset from a review forum in www.wdzj.com which is the leading and largest portal of peer to peer lending industry in China, we get the following interesting results. First, due to the manipulation of review helpfulness, a manipulated positive review is more likely to receive higher helpfulness, while a manipulated negative is more likely to get lower helpfulness. Second, a manipulated review tends to be lower quality in terms of readability and word count, which are found as positive predictors for review helpfulness. Third, high quality firms tend to manipulate more positive reviews, and at the same time high quality firms will receive more negative manipulated reviews. This study extends current understanding about online review manipulation, thereby providing theoretical and practice implications

Identification of Opinion Spammers using Reviewer Reputation and Clustering Analysis

Online reviews have increasingly become a very important resource before making a purchasing decisions. Unfortunately, malicious sellers try to game the system by hiring a person or team (which is called spammers) to fabricate fake reviews to improve their reputation.Existing methods mainly take the problem as a general binary classification or focus on some heuristic rules. However, supervised learning methods relies heavily on a large number of labeled examples of deceptive and truthful opinions by domain experts, and most of features mentioned in the heuristic strategy ignore the characteristic of the group organization among spammers. In this paper, an effective method of identifying opinion spammers is proposed. Firstly, suspected spammers are detected by means of unsupervised learning based on reviewer’s reputation. We believe that the reviewer’s reputation has a direct relation with the quality of reviews. Generally, review written by user with lower reputation, shows lower quality and higher possibility to be fake. Therefore, the model assigns reputation score to each reviewer wherein the content based factors and activeness of reviewers are employed efficiently. On basis of all suspected spammers, k-center clustering algorithm is performed to further spot the spammers based on the observation of burst of review release time. Experimental results on Amazon’s dataset are encouraging and indicate that our approach poses high accuracy and recall, and good performance is achieved

A Multi-type Classifier Ensemble for Detecting Fake Reviews Through Textualbased Feature Extraction

The financial impact of online reviews has prompted some fraudulent sellers to generate fake consumer reviews for either promoting their products or discrediting competing products. In this study, we propose a novel ensemble model - the Multitype Classifier Ensemble (MtCE) - combined with a textual-based featuring method, which is relatively independent of the system, to detect fake online consumer reviews. Unlike other ensemble models that utilise only the same type of single classifier, our proposed ensemble utilises several customised machine learning classifiers (including deep learning models) as its base classifiers. The results of our experiments show that the MtCE can adequately detect fake reviews, and that it outperforms other single and ensemble methods in terms of accuracy and other measurements in all the relevant public datasets used in this study. Moreover, if set correctly, the parameters of MtCE, such as base-classifier types, the total number of base classifiers, bootstrap and the method to vote on output (e.g., majority or priority), further improve the performance of the proposed ensemble

Man vs machine – Detecting deception in online reviews

This study focused on three main research objectives: analyzing the methods used to identify deceptive online consumer reviews, evaluating insights provided by multi-method automated approaches based on individual and aggregated review data, and formulating a review interpretation framework for identifying deception. The theoretical framework is based on two critical deception-related models, information manipulation theory and self-presentation theory. The findings confirm the interchangeable characteristics of the various automated text analysis methods in drawing insights about review characteristics and underline their significant complementary aspects. An integrative multi-method model that approaches the data at the individual and aggregate level provides more complex insights regarding the quantity and quality of review information, sentiment, cues about its relevance and contextual information, perceptual aspects, and cognitive material