In this paper, we describe a methodology to infer Bullish or Bearish sentiment towards companies brands. More specifically, our approach leverages affective lexica and word embeddings in combination with convolutional neural networks to infer the sentiment of financial news headlines towards a target company. Such architecture was used and evaluated in the context of the SemEval 2017 challenge (task 5, subtask 2), in which it obtained the best performance.
@cite_0 , contains 10k words taken from ConceptNet and aligned with WordNetAffect, and extends the latter to concepts like have breakfast'. @cite_4 contains roughly 4k lemma #PoS manually annotated by one linguist using 80 emotion labels. @cite_22 contains almost 10k lemmas annotated with an intensity label for each emotion using Mechanical Turk. Finally, is an extension of @cite_2 and contains 2.5k words in the form lemma #PoS . The latter is the only lexicon providing words annotated also with emotion scores rather than only with labels.
@cite_0 @cite_4 @cite_22 @cite_2
2141243797 2040467972 1595791987
We propose a convenient fusion of natural-language processing and fuzzy logic techniques for analyzing affect content in free text; our main goals are fast analysis and visualization of affect content for decision-making. The primary linguistic resource for fuzzy semantic typing is the fuzzy affect lexicon, from which other important resources are generated, notably the fuzzy thesaurus and affect category groups. Free text is tagged with affect categories from the lexicon, and the affect categories' centralities and intensities are combined using techniques from fuzzy logic to produce affect sets fuzzy sets that represent the affect quality of a document. We show different aspects of affect analysis using news stories and movie reviews. Our experiments show a very good correspondence of affect sets with human judgments of affect content. We ascribe this to the effective representation of ambiguity in our fuzzy affect lexicon, and the ability of fuzzy logic to deal successfully with the ambiguity of words in natural language. Planned extensions of the system include personalized profiles for Web-based content dissemination, fuzzy retrieval, clustering and classification. Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper, we show how the combined strength and wisdom of the crowds can be used to generate a large, high-quality, word–emotion and word–polarity association lexicon quickly and inexpensively. We enumerate the challenges in emotion annotation in a crowdsourcing scenario and propose solutions to address them. Most notably, in addition to questions about emotions associated with terms, we show how the inclusion of a word choice question can discourage malicious data entry, help to identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help to obtain annotations at sense level (rather than at word level). We conducted experiments on how to formulate the emotion-annotation questions, and show that asking if a term is associated with an emotion leads to markedly higher interannotator agreement than that obtained by asking if a term evokes an emotion. In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging. The evolving nature of language in online conversations is a main issue in affect sensing from this media type, since sentence parsing might fail while syntactical structure analysis. The developed Affect Analysis Model was designed to handle not only correctly written text, but also informal messages written in abbreviated or expressive manner. The proposed rule-based approach processes each sentence in sequential stages, including symbolic cue processing, detection and transformation of abbreviations, sentence parsing, and word phrase sentence-level analyses. In a study based on 160 sentences, the system result agrees with at least two out of three human annotators in 70 of the cases. In order to reflect the detected affective information and social behaviour, an avatar was created.