Comparison of Sentiment Analysis and User Ratings in Venue Recommendation

Venue recommendation aims to provide users with venues to visit, taking into account historical visits to venues. Many venue recommendation approaches make use of the provided users’ ratings to elicit the users’ preferences on the venues when making recommendations. In fact, many also consider the users’ ratings as the ground truth for assessing their recommendation performance. However, users are often reported to exhibit inconsistent rating behaviour, leading to less accurate preferences information being collected for the recommendation task. To alleviate this problem, we consider instead the use of the sentiment information collected from comments posted by the users on the venues as a surrogate to the users’ ratings. We experiment with various sentiment analysis classifiers, including the recent neural networks-based sentiment analysers, to examine the effectiveness of replacing users’ ratings with sentiment information. We integrate the sentiment information into the widely used matrix factorization and GeoSoCa multi feature-based venue recommendation models, thereby replacing the users’ ratings with the obtained sentiment scores. Our results, using three Yelp Challenge-based datasets, show that it is indeed possible to effectively replace users’ ratings with sentiment scores when state-of-the-art sentiment classifiers are used. Our findings show that the sentiment scores can provide accurate user preferences information, thereby increasing the prediction accuracy. In addition, our results suggest that a simple binary rating with ‘like’ and ‘dislike’ is a sufficient substitute of the current used multi-rating scales for venue recommendation in location-based social networks

Latent sentiment model for weakly-supervised cross-lingual sentiment classification

In this paper, we present a novel weakly-supervised method for crosslingual sentiment analysis. In specific, we propose a latent sentiment model (LSM) based on latent Dirichlet allocation where sentiment labels are considered as topics. Prior information extracted from English sentiment lexicons through machine translation are incorporated into LSM model learning, where preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. An efficient parameter estimation procedure using variational Bayes is presented. Experimental results on the Chinese product reviews show that the weakly-supervised LSM model performs comparably to supervised classifiers such as Support vector Machines with an average of 81% accuracy achieved over a total of 5484 review documents. Moreover, starting with a generic sentiment lexicon, the LSM model is able to extract highly domainspecific polarity words from text

Learning user and product distributed representations using a sequence model for sentiment analysis

In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-The-Art results on these benchmark datasets

Predicting Fraud Apps Using Hybrid Learning Approach: A Survey

Each individual in the planet are mobile phone users in fact smart-phone users with android applications. So, due to this attractiveness and well-known concept there will be a hasty growth in mobile technology. And in addition in information mining, mining the required information from a fastidious application is exceptionally troublesome. Consolidating these two ideas of ranking frauds in android market and taking out required information is gone exceptionally tough.The mobile phone Apps has developed at massive speed in some years; as for march 2017, there are nearby 2.8 million Apps at google play and 2.2 Apps at Google Apps store. In addition, there are over 400,000 self-governing app developers all fighting for the attention of the same potential clients. The Google App Store saw 128,000 new business apps alone in 2014 and the mobile gaming category alone has contest to the tune of almost 300,000 apps. Here the major need to make fraud search in Apps is by searching the high ranked applications up to 30-40 which may be ranked high in some time or the applications which are in those high ranked lists should be confirmed but this is not applied for thousands of applications added per day. So, go for wide examination by applying some procedure to every application to judge its ranking. Discovery of ranking fraud for mobile phone applications, require a flawless, fraud less and result that show correct application accordingly provide ranking; where really make it occur by searching fraud of applications. They create fraud of App by ranked high the App by methods using such human water armies and bot farms; where they create fraud by downloading application through different devices and provide fake ratings and reviews. So, extract critical data connecting particular application such as review which was called comments and lots of other information, to mine and place algorithm to identify fakeness in application rank

Context Driven Bipolar Adjustment for Optimized Aspect Level Sentiment Analysis

122–127World Wide Web provides numerous opinionated data that can influence users. Reviews on online data highly affect the user’s perception while buying a particular or related product from an online shopping site. The online review provided by a customer helps other customers to make up their decision regarding purchasing that item. Looking at the developer’s and producer’s perspective, the opinions of customers on their manufactured items is helpful in identifying deformities as well as scope for improving its quality. Equipped with all this information, the product can be developed and managed more efficiently. Along with the overall rating of the product, the feature-based rating will have a great impact on the decision-making process of the customer. In this paper, an optimized scheme of aspect level sentiment analysis is presented to analyze the online reviews of a product. Reviews ratings have been used for learning approach. Inherently biased reviews are considered to optimize the Aspect Level Sentiment Analysis. Bi-polar aspect level sentiment analysis model has been trained using multiple kernels of support vector machine to optimize the results. Lexicon based aspect level sentiment analysis is performed first and later on the basis of bipolar words adjustment, and its effect on results, aspect level sentiment analysis for efficient optimization has been performed. A Web Crawler is developed to extract data from Amazon. The results obtained outperformed traditional lexicon based Aspect Level Sentiment Analysis