On Mining Conditions using Encoder-decoder Networks

A condition is a constraint that determines when something holds. Mining them is paramount to understanding many sentences properly. There are a few pattern-based approaches that fall short because the patterns must be handcrafted and it is not easy to characterise unusual ways to express conditions; there is one machine-learning approach that requires specific-purpose dictionaries, taxonomies, and heuristics, works on opinion sentences only, and was evaluated on a small dataset with Japanese sentences on hotels. In this paper, we present an encoder-decoder model to mine conditions that does not have any of the previous drawbacks and outperforms the state of the art in terms of effectiveness.Ministerio de Economía y Competitividad TIN2013-40848-

Will Twitter Make You a Better Investor? A Look at Sentiment, User Reputation and Their Effect on the Stock Market

The use of social networks like Twitter and Facebook has grown exponentially over the last few years. Twitter, which was founded in 2006, had an estimated 200 million users on January 1 2011 with more than 95 million tweets sent per day. With this rapid growth and significant adoption, Twitter has become an important tool for businesses and individuals to communicate and share information. In addition, Twitter has rapidly grown as a medium to share ideas and thoughts on investing decisions. This research builds on prior published research and attempts to determine whether there is correlation between twitter and the stock market by studying sentiment, message volume, price movement and stock volume as well as the affect that a twitter user’s reputation may have on sentiment and the stock market

Impact of personalized review summaries on buying decisions: An experimental study

This study evaluates the impact of personalization of review summaries on consumers’ cognitive efforts and buying decision. Following an experimental procedure we tested four hypotheses pertaining to online buyers’ decision process. Our results show that personalized review summary significantly reduces the information processing effort and information requirements of those who received personalized review summaries as compared to those who did not. This study thus contributes to e-commerce literature on online buyer behavior and recommender systems strategy

Discourse structure and attitudinal valence of opinion words in sentiment extraction

See PDF

Sentiment Analysis: Comparative Analysis of Multilingual Sentiment and Opinion Classification Techniques

Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study