Improved lexicon-based sentiment analysis for social media analytics

Social media channels, such as Facebook or Twitter, allow for people to express their views and opinions about any public topics. Public sentiment related to future events, such as demonstrations or parades, indicate public attitude and therefore may be applied while trying to estimate the level of disruption and disorder during such events. Consequently, sentiment analysis of social media content may be of interest for different organisations, especially in security and law enforcement sectors. This paper presents a new lexicon-based sentiment analysis algorithm that has been designed with the main focus on real time Twitter content analysis. The algorithm consists of two key components, namely sentiment normalisation and evidence-based combination function, which have been used in order to estimate the intensity of the sentiment rather than positive/negative label and to support the mixed sentiment classification process. Finally, we illustrate a case study examining the relation between negative sentiment of twitter posts related to English Defence League and the level of disorder during the organisation’s related events.

Generating a Word-Emotion Lexicon from #Emotional Tweets

Research in emotion analysis of text suggest that emotion lexicon based features are superior to corpus based n-gram features. However the static nature of the general purpose emotion lexicons make them less suited to social media analysis, where the need to adopt to changes in vocabulary usage and context is crucial. In this paper we propose a set of methods to extract a word-emotion lexicon automatically from an emotion labelled corpus of tweets. Our results confirm that the features derived from these lexicons outperform the standard Bag-of-words features when applied to an emotion classification task. Furthermore, a comparative analysis with both manually crafted lexicons and a state-of-the-art lexicon generated using Point-Wise Mutual Information, show that the lexicons generated from the proposed methods lead to significantly better classi- fication performance.

Lexicon ecclesiasticum latino-hispanicum, ex sacris Bibliis, conciliis, pontificum decretis, ac theologorum placitis, divorum vitis, variisdictionariis, aliisque probatissimis scriptoribus concinnatum, servataubique vera etymologiae, ortographiae, et accentus ratione

UANL

Lexicon peripateticum philosophico-theologicum, in quo sholastico rum...

UANL

The Unsupervised Acquisition of a Lexicon from Continuous Speech

We present an unsupervised learning algorithm that acquires a natural-language lexicon from raw speech. The algorithm is based on the optimal encoding of symbol sequences in an MDL framework, and uses a hierarchical representation of language that overcomes many of the problems that have stymied previous grammar-induction procedures. The forward mapping from symbol sequences to the speech stream is modeled using features based on articulatory gestures. We present results on the acquisition of lexicons and language models from raw speech, text, and phonetic transcripts, and demonstrate that our algorithm compares very favorably to other reported results with respect to segmentation performance and statistical efficiency.

Knowledge and use of the lexicon in French as a second language

This special issue of JFLS focuses on what learners know about French words, on how they use that knowledge and on how it can be investigated and assessed. In many ways, it is a sequel to the special issue on the Acquisition of French as a Second Language edited by Myles and Towell that appeared in JFLS in 2004. While articles on the L2 acquisition of the French lexicon have appeared in a variety of journals, including JFLS, this special issue (SI) is the first volume which specifically focuses on lexical knowledge and use among learners of French as a second language. The issue is timely, because of the growing importance of vocabulary in the SLA research agenda, but also because research into vocabulary acquisition appears at the top of a list of areas in which teachers of Modern Foreign Languages are most interested.

CUP Intermediate Greek-English Lexicon

Document design and typeface design: A typographic specification for a new Intermediate Greek-English Lexicon by CUP, accompanied by typefaces modified for the specific typographic requirements of the text. The Lexicon is a substantial (over 1400 pages) publication for HE students and academics intended to complement Liddell-Scott (the standard reference for classical Greek since the 1850s), and has been in preparation for over a decade. The typographic appearance of such works has changed very little since the original editions, largely to the lack of suitable typefaces: early digital proofs of the Lexicon utilised directly digitised versions of historical typefaces, making the entries difficult to navigate, and the document uneven in typographic texture. Close collaboration with the editors of the Lexicon, and discussion of the historical precedents for such documents informed the design at all typographic levels to achieve a highly reader-friendly results that propose a model for this kind of typography. Uniquely for a work of this kind, typeface design decisions were integrated into the wider document design specification. A rethinking of the complex typography for Greek and English based on historical editions as well as equivalent bilingual reference works at this level (from OUP, CUP, Brill, Mondadori, and other publishers) led a redefinition of multi-script typeface pairing for the specific context, taking into account recent developments in typeface design. Specifically, the relevant weighting of elements within each entry were redefined, as well as the typographic texture of type styles across the two scripts. In details, Greek typefaces were modified to emphasise clarity and readability, particularly of diacritics, at very small sizes. The relative weights of typefaces typeset side-by-side were fine-tuned so that the visual hierarchy of the entires was unambiguous despite the dense typesetting.

Computational linguistic systematization of a lexicon from the ibitinga embroidery industry conceptual domain

This work discusses a proposition for organizing the lexical items from the conceptual domain labeled THE EMBROIDERY INDUSTRY OF IBITINGA in terms of a natural ontology. It also aims to establish the alignment between this ontology and the bases WordNet.Pr and WordNet.Br. © 2009 IEEE.

Critical behavior in a cross-situational lexicon learning scenario

The associationist account for early word learning is based on the co-occurrence between referents and words. Here we introduce a noisy cross-situational learning scenario in which the referent of the uttered word is eliminated from the context with probability gamma, thus modeling the noise produced by out-of-context words. We examine the performance of a simple associative learning algorithm and find a critical value of the noise parameter gamma(c) above which learning is impossible. We use finite-size scaling to show that the sharpness of the transition persists across a region of order tau(-1/2) about gamma(c), where tau is the number of learning trials, as well as to obtain the learning error (scaling function) in the critical region. In addition, we show that the distribution of durations of periods when the learning error is zero is a power law with exponent -3/2 at the critical point. Copyright (C) EPLA, 2012