This paper describes the system implemented by Fundació Barcelona Media (FBM) for classifying the polarity of opinion expressions in tweets and SMSs, and which is supported by a UIMA pipeline for rich linguistic and sentiment annotations. FBM participated in the SEMEVAL 2013 Task 2 on polarity classification. It ranked 5th in Task A (constrained track) using an ensemble system combining ML algo...

Structural correspondence learning (SCL) is an effective method for cross-lingual sentiment classification. This approach uses unlabeled documents along with a word translation oracle to automatically induce task specific, cross-lingual correspondences. It transfers knowledge through identifying important features, i.e., pivot features. For simplicity, however, it assumes that the word translat...

This paper presents a simple, robust and (almost) unsupervised dictionary-based method, qwn-ppv (Q-WordNet as Personalized PageRanking Vector) to automatically generate polarity lexicons. We show that qwn-ppv outperforms other automatically generated lexicons for the four extrinsic evaluations presented here. It also shows very competitive and robust results with respect to manually annotated o...

Sentiment is central to many studies of communication science, from negativity and polarization in political analyzing product reviews social media comments other sub-fields. This study provides an exhaustive comparison sentiment analysis methods, using a validation set Dutch economic headlines compare the performance manual annotation, crowd coding, numerous dictionaries machine learning both ...

Practitioners and researchers alike increasingly use social media messages as an additional source of information to analyse stock price movements. In this regard, previous preliminary findings demonstrate the incremental value of considering the multi-dimensional structure of human emotions in sentiment analysis instead of the predominant assessment of the binary positive-negative valence of e...

Advances in NLP techniques have led to a great demand for tagging and analysis of the sentiments from unstructured natural language data over the last few years. A typical approach to sentiment analysis is to start with a lexicon of positive and negative words and phrases. In these lexicons, entries are tagged with their prior out of context polarity. Unfortunately all efforts found in literatu...

Faculty of Engineering, Mathematics and Science School of Engineering Masters in Computer Engineering Weather and sentiment analysis in the commodities market by Gary White This project involves an investigation of the impact of the effect of exogenous and qualitative analysis on the spot price of commodities. The commodities market is a complex system and to estimate the effect of weather and ...

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.02.109 q This work is supported by China National Ke (2008BAH26B00). * Corresponding author. Tel.: +86 571 87953955. E-mail address: [email protected] (G. Qiu). Online advertising has become one of the major revenue sources of today’s Internet ecosystem. The main advertising channels used to distribute textual ads are s...

Nowadays, social contacts are vital to find relevant content. We need to connect with people with similar interests because they provide content that matters. Every day is more clear that in the future of document recommendations will be necessary to cross the traditional data with the data obtained from social networks. For instance, in order to provide the best content available we can use se...

Bilingual dictionary use in L2 writing test performance has recently been the subject of debate. Opinions differ according to how the trait is understood and whether the system favors the process-oriented or product-oriented views towards the assessment and writing skill. Given the need for more empirical support, this study is aimed at investigating the availability of bilingual dictionary use...