Improve the effectiveness of the opinion retrieval and opinion polarity classification

Opinion retrieval is a document retrieving and ranking process. A relevant document must be relevant to the query and contain opinions toward the query. Opinion polarity classification is an extension of opinion retrieval. It classifies the retrieved document as positive, negative or mixed, according to the overall polarity of the query relevant opinions in the document. This paper (1) proposes several new techniques that help improve the effectiveness of an existing opinion retrieval system; (2) presents a novel two-stage model to solve the opinion polarity classification problem. In this model, every query relevant opinionated sentence in a document retrieved by our opinion retrieval system is classified as positive or negative respectively by a SVM classifier. Then a second classifier determines the overall opinion polarity of the document. Experimental results show that both the opinion retrieval system with the proposed opinion retrieval techniques and the polarity classification model outperformed the best reported systems respectively. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Searc

Analyzing user-generated online content for drug discovery: Development and use of MedCrawler

Motivation: Ethnopharmacology, or the scientific validation of traditional medicine, is a respected starting point in drug discovery. Home remedies and traditional use of plants are still widespread, also in Western societies. Instead of perusing ancient pharmacopeias, we developed MedCrawler, which we used to analyze blog posts for mentions of home remedies and their applications. This method is free and accessible from the office computer. Results: We developed MedCrawler, a data mining tool for analyzing user-generated blog posts aiming to find modern 'traditional' medicine or home remedies. It searches user-generated blog posts and analyzes them for correlations between medically relevant terms. We also present examples and show that this method is capable of delivering both scientifically validated uses as well as not so well documented applications, which might serve as a starting point for follow-up research.Peer reviewe

Features of trolling in online comments to the news article

© 2018 Academic Research Publishing Group. In the present study, we objectify the timeliness of studying trolling as a type of communicative behavior during Internet discourse. Different approaches to the concept interpretation are being considered. Trolling is defined by the author as a purposeful and motivated communicative behavior, aimed to the media scene destabilization. The comments under one of the French news article demonstrate two basic techniques of trolling: subject-oriented and object-oriented ones. Within the frameworks of object-oriented technology there are the offtoping tactics and elfing being analyzed. The subject-oriented technique is based on the individual discrediting of the person: pointing out and highlighting the incompetence of the interlocutor, the ironic expression of disagreement with his point of view, and elfing. Studying various methods of trolling makes it possible for us to distinguish four types of trolls in Internet comments: a provocateur troll, an offtoper troll, a demagogue troll and an elf troll. All of them have the same single goal to stir up the flame and enjoy the commentator's feedback. One makes a conclusion about the necessity of a selective approach to reading the comments, with the purpose to avoid trolls, who choose their speech tactics depending on their intentions

Opinion mining of online customer reviews

Customer Opinions play a very crucial role in daily life. When we have to take a decision, opinions of other individuals are also considered. Now-a-days many of web users post their opinions for many products through blogs, review sites and social networking sites. Business organizations and corporate organizations are always eager to find consumer or individual views regarding their products, support and service. In e-commerce, online shopping and online tourism, its very crucial to analyse the good amount of social data present on the Web automatically therefore, its very important to create methods that automatically classify them. Opinion Mining sometimes called as Sentiment Classification is defined as mining and analysing of reviews, views, emotions and opinions automatically from text, big data and speech by means of various methods. In this thesis we are going to see how Apriori frequent item set mining algorithm can be used for mining reviews from online reviews those are posted by customers. Our main theme is to create a system for analysing opinions which implies judgement of different consumer products

Résumé automatique de textes d'opinion

International audienceIn this paper, we present a summarization system that is specifically designed to process blog posts, where factual information is mixed with opinions on the discussed facts. Our approach combines redundancy analysis with new information tracking and is enriched by a module that computes the polarity of textual fragments in order to summarize blog posts more efficiently. The system is evaluated against English data, especially through the participation in TAC (Text Analysis Conference), an international evaluation framework for automatic summarization, in which our system obtained interesting results.Nous présentons dans cet article un système de résumé automatique tourné vers l'analyse de blogs, où sont exprimées à la fois des informations factuelles et des prises de position sur les faits considérés. Notre système de résumé est fondé sur une approche nouvelle qui mêle analyse de la redondance et repérage des informations nouvelles dans les textes ; ce système générique est en outre enrichi d'un module de calcul de la polarité de l'opinion véhiculée afin de traiter de façon appropriée la subjectivité qui est le propre des billets de blogs. Le système est évalué sur l'anglais, à travers la participation à la campagne d'évaluation internationale TAC (Text Analysis Conference) où notre système a obtenu des performances satisfaisantes