Identifying Review Polarity: This step is to determine the polarity of opinion words, and then to

For experimental purpose dataset has been obtained from MovieLens web site (http://movielens.org) which is collected and made available by GroupLens research. The MovieLens dataset includes 100,000 ratings ranging from 1 to 5, with a total of 943 users and 1682 films. In this research, randomly select 1100 users with more than 30 ratings. Also, LIBRARY THING dataset includes a book rating data set comprising more than 1.1 million ratings (scales 1-10) by 7279 users on 37,232 books. We have selected 5000 tags from the top to recommend books. The user-item dataset is available in the form of two dimensional

Recently I bought a pair of skis online for the upcoming ski season. The only limit to the skis listed was the filters I set on individual websites. There were hundreds to choose from even after I had filtered the results, so to understand what would fit my uses best I would need to understand the skis. I researched the styles of skis and what their best uses were for. When there was one that seemed to fit my needs and style, I would then look at reviews from individuals who had used the product before. User reviews give specific information on a product that the item’s description does not have. Many of the reviews that I read would praise a certain aspect of the product or point of a flaw that they experienced during use. While researching a product was pretty straightforward, I have not always had such smooth experiences researching.

Bi et al \cite{Rec:Bi} provides ranked related entities to the user query along with the results of the main entity. In order to do this, this articles makes use of user's search history, click history and knowledge base. A matrix is created comprising of the user information which connects to the entities along with the ranking, click results. A tri-linear function\cite{Rec:Bi} is defined mapping these details and which will be used to rank the related entities

An overall review score is already assigned by the user when he/she writes the reviews. From these, the proposed probabilistic model calculates the aspect score for each aspect from the user's list of preferences and presents those reviews that has the best aspect score from different reviews. The model implicitly predicts the user aspects and their scores from the review provided by the user and then mines for the reviews that match the aspect score similar to those of the

Recommender Systems are based on well-structured and incremental algorithms that have different strengths and weaknesses \cite{ricci2011introduction}. In general, the main existing RSs are concerned with improving accuracy with the premise of being useful to users. In this context, there is an increasing number of custom techniques that analyze the profile of the target user in order to better satisfy them \cite{bobadilla2013recommender}. In this sense, we conducted a broad study of the several metrics used in the literature to evaluate RSs. By organizational purposes, we divided these metrics into three groups: {\it Effectiveness-based}, {\it Complementary Dimensions of Quality} and {\it Domain Profiling}.\looseness=-1

This section of a report that is made up of 15pages of inspection report it is commenting on how well the

Many people tend to look for information about places or products before paying for them. Modern technology has made an abundance of reviews and the process of finding them easier. With all of this information on the internet, it could be difficult to distinguish what is a good review.

Personally, the more important steps in the peer review cycle are: the ideas, the literature review and publication steps. Ideas are important because that is what starts the process; it allows for creativity, ingenuity, and advancements all around. Ideas are the culmination of a researcher’s dreams and desires and without those, things like medications, cars, pacemakers and the like would never be created. However, ideas are sometimes hard to come by, at times one must look at what others have done and identify gaps or improvements that can be made. What good would it do to repeatedly publish data and perform experiments that cost fortunes yet yield similar if not the same data; I say nothing at all, that is why the literature review step is also important. It is a vital checkpoint that assures time and money

Judges are the most prestigious members of the court system, but they are faced with challenges that reflect upon there ethics, and faithfulness to the people they serve. Often judges make careless misstates and forget that they are appointed or voted in by the public. Therefore some judges have been removed from the bench due to their behavior that includes, sexual allegations, taking cash, making racist and sexist comments, and lying to a grand jury just to name a few. Voters elect some judges, governors or the president of the United States appoints some and others are appointed through a merit selection.

Some disciplines require that you use information that is as current as possible. In the sciences, for instance, treatments for medical problems are constantly changing according to the latest studies. Information even two years old could be obsolete. However, if you are writing a review in the humanities, history, or social sciences, a survey of the history of the literature may be what is needed, because what is important is how perspectives have changed through the years or within a certain time period. Try sorting through some other current bibliographies or literature reviews in the field to get a sense of what your discipline expects. You can

Various techniques have been used in content-based models. Such systems try to find regularities in the descriptions that can be used to distinguish highly rated items from others [97]. Content-based approaches are based on objective information about the items. This information is automatically extracted from various sources (e.g., Web pages) or manually introduced (e.g., product database). However, selecting one item or another is based mostly on subjective attributes of the item (e.g., a well-written document or a product with a spicy taste). Therefore, these attributes, which better influence the user’s choice, are not taken into account. In the rest of this section, we discuss three technique of content-based filtering technique including keyword-based models, semantic techniques, and probabilistic models. The first systematic evaluation of the impact of applying perturbation-based privacy technologies on the usability of content-based recommendation systems proposed by Puglisi, S., et al. (2015) [98]. The primary goal of their work is to investigate the effects of tag forgery to the content-based recommendation in a real-world application scenario, studying the interplay between the degree of privacy and the potential degradation of the quality of the recommendation. In other paper, Rana, C. and S.K. Jain [23] have developed a book recommendation system that is based on content-based recommendation technique and takes into account the choices of not an

▪ This structure creates bias with the wording. A numerical scale from 1-5 or 1-7 would be ideal.

1. You have to submit an original quote or paraphrased quote from a third-party website that Google considers reputable. The extension will then include a link to the website where the review can be found.

For the problem solving approach we first consider some base points like existing approaches, algorithms etc . The overall ranking is made based on Frequent comments of Consumers about overall opinion on that product. The ranking is calculated using term frequency and other formulas for positive and negative comments.

Some combined rule algorithms were proposed in (Medhat et al., 2008a). Therefore, a study on decision tree and decision rule problem is done by Quinlan (1986). Probabilistic Classifier Probabilistic classifiers make the utilization of blend of models for classification. Every class is considered to be a component of the mixed model. We have described various probabilistic classifiers for sentiment analysis problem in the next subsection. 4.1.1.4.1 Naive Bayes Classifier (NB). It is the frequently used classifier in sentiment analysis. In sentiment analysis, naive Bayes classifier calculates the posterior probability of either positive class or negative class depending on the sentiment words distributed over the document. The work of naïve Bayes classifier is based on the Bag-of-word extraction of features in which the word’s position is overlooked in the whole text. This classifier uses the Bayes theorem. It calculates the probability for the sentiment word in a document and tells whether that word belongs to the positive or negative class. The probability can be calculated using the given formula. This assumption results in Bayesian Network. A Bayesian network is a directed acyclic graph containing nodes and edges, where nodes denote the random variables and the edges denote the conditional dependencies. It is a conditional exponential classifier that takes the feature sets with label and converts them into