Multi-facet rating of online hotel reviews: issues, methods and experiments

Online product reviews are becoming increasingly popular, and are being used more and more frequently by consumers in order to choose among competing products. Tools that rank competing products in terms of the satisfaction of consumers that have purchased the product before, are thus also becoming popular. We tackle the problem of rating (i.e., attributing a numerical score of satisfaction to) consumer reviews based on their tex- tual content. In this work we focus on multi-facet rating of hotel reviews, i.e., on the case in which the review of a hotel must be rated several times, according to several aspects (e.g., cleanliness, dining facilities, centrality of location). We explore several aspects of the problem, including the vectorial representation of the text based on sentiment analysis, collocation analysis, and feature selection for ordinal-regression learning. We present the results of experiments conducted on a corpus of approximately 15,000 hotel reviews that we have crawled from a popular hotel review site

Aspect Mining for Drug Recommendation: A Survey

Now a days due to this computerized world all the information related to the patients queries are available on internet. This survey paper compares various research issues and few techniques related to the user query for their drug discovery. These reviews helps users to know more about the drug dosage, their side-effects and also specifications. Reviews provides positive as well as negative feedback, Hence these reviews also plays an important role for patients and pharmaceutical industries. The probabilistic aspect mining model (PAMM) identifies aspects according to the class labels. PAMM finds aspects related to one class instead of finding aspects for all classes simultaneously in each execution. PAMM also find aspects measured using the mean point wise mutual information .Hence mixing concepts of different class label gets avoided

Harvesting and summarizing user-generated content for advanced speech-based human-computer interaction

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 155-164).There have been many assistant applications on mobile devices, which could help people obtain rich Web content such as user-generated data (e.g., reviews, posts, blogs, and tweets). However, online communities and social networks are expanding rapidly and it is impossible for people to browse and digest all the information via simple search interface. To help users obtain information more efficiently, both the interface for data access and the information representation need to be improved. An intuitive and personalized interface, such as a dialogue system, could be an ideal assistant, which engages a user in a continuous dialogue to garner the user's interest and capture the user's intent, and assists the user via speech-navigated interactions. In addition, there is a great need for a type of application that can harvest data from the Web, summarize the information in a concise manner, and present it in an aggregated yet natural way such as direct human dialogue. This thesis, therefore, aims to conduct research on a universal framework for developing speech-based interface that can aggregate user-generated Web content and present the summarized information via speech-based human-computer interaction. To accomplish this goal, several challenges must be met. Firstly, how to interpret users' intention from their spoken input correctly? Secondly, how to interpret the semantics and sentiment of user-generated data and aggregate them into structured yet concise summaries? Lastly, how to develop a dialogue modeling mechanism to handle discourse and present the highlighted information via natural language? This thesis explores plausible approaches to tackle these challenges. We will explore a lexicon modeling approach for semantic tagging to improve spoken language understanding and query interpretation. We will investigate a parse-and-paraphrase paradigm and a sentiment scoring mechanism for information extraction from unstructured user-generated data. We will also explore sentiment-involved dialogue modeling and corpus-based language generation approaches for dialogue and discourse. Multilingual prototype systems in multiple domains have been implemented for demonstration.by Jingjing Liu.Ph.D

Feature selection for ordinal regression

Ordinal regression (also known as ordinal classification) is a supervised learning task that consists of automatically determining the implied rating of a data item on a fixed, discrete rating scale. This problem is receiving increasing attention from the sentiment analysis and opinion mining community, due to the importance of automatically rating increasing amounts of product review data in digital form. As in other supervised learning tasks such as (binary or multiclass) classification, feature selection is needed in order to improve efficiency and to avoid overfitting. However, while feature selection has been extensively studied for other classification tasks, is has not for ordinal regression. In this paper we present four novel feature selection metrics that we have specifically devised for ordinal regression, and test them on two datasets of product review data