Negation Modeling for German Polarity Classification

Wiegand, Michael; Wolf, Maximilian; Ruppenhofer, Josef

doi:10.1007/978-3-319-73706-5_9

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Category 3: Emotional versus Non-Emotional -A sentence can carry positive, negative or neutral emotional content, which can regulate neural pathway of perceiving and analyzing that sentence in brain (Vuilleumier, 2005). In this study, dictionary of sentiment, Germanlex which comes from PolArt (Klenner, Fahrni, & Petrakis, 2009) and was used at Polcla 1 project (Wiegand, Wolf, & Ruppenhofer, 2017) was used. It contains information about the sentiment that each word expresses, the polarity of that sentiment (positive, negative or neutral) as well as intensity of sentiment which is a value between −1 and 1 (Klenner et al, 2009).…”

Section: Linguistics Featuresmentioning

confidence: 99%

Semantic and BCI-performance in completely paralyzed patients: Possibility of language attrition in completely locked in syndrome

et al. 2019

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Section: Linguistics Featuresmentioning

confidence: 99%

Semantic and BCI-performance in completely paralyzed patients: Possibility of language attrition in completely locked in syndrome

et al. 2019

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SentiCode: A new paradigm for one-time training and global prediction in multilingual sentiment analysis

Kanfoud

Bouramoul

2022

J Intell Inf Syst

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The main objective of multilingual sentiment analysis is to analyze reviews regardless of the original language in which they are written. Switching from one language to another is very common on social media platforms. Analyzing these multilingual reviews is a challenge since each language is different in terms of syntax, grammar, etc. This paper presents a new language-independent representation approach for sentiment analysis, SentiCode. Unlike previous work in multilingual sentiment analysis, the proposed approach does not rely on machine translation to bridge the gap between different languages. Instead, it exploits common features of languages, such as part-of-speech tags used in Universal Dependencies. Equally important, SentiCode enables sentiment analysis in multi-language and multi-domain environments simultaneously. Several experiments were conducted using machine/deep learning techniques to evaluate the performance of SentiCode in multilingual (English, French, German, Arabic, and Russian) and multi-domain environments. In addition, the vocabulary proposed by SentiCode and the effect of each token were evaluated by the ablation method. The results highlight the 70% accuracy of SentiCode, with the best trade-off between efficiency and computing time (training and testing) in a total of about 0.67 seconds, which is very convenient for real-time applications.

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Negation of German polar words and expressions in automated analysis of text tonality

Glushak

2023

Philology. Theory & Practice

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The aim of the study is to describe various groups of negative words in the German language and the features of their functioning in text tonality analysis in the framework of automated natural language processing (NLP). Scientific analysis focuses on the ability of negative words to modify polar words and expressions, i.e., to change their connotation within an utterance. The study is novel in that it is the first to present in a systematic manner the syntactic-morphological relationship between certain groups of linguistic units of negation and polar words, which must be taken into account in NLP when carrying out sentiment analysis of German texts based on lexico-grammatical rules. As a result, all German language means of negation were divided into groups depending on the extent of their impact on the polar words in the sentence: tonality modification for only the adjoining polar element or the ability to interact with the parts of an utterance that are located at a distance of several tokens. For automatic tonality analysis of German texts, it is necessary to mark the morphological and syntactic features reflecting the peculiarities of both the selected groups of negation words and polar words and expressions at the stage of feature extraction.

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Negation Modeling for German Polarity Classification

Cited by 3 publications

References 14 publications

Semantic and BCI-performance in completely paralyzed patients: Possibility of language attrition in completely locked in syndrome

Semantic and BCI-performance in completely paralyzed patients: Possibility of language attrition in completely locked in syndrome

SentiCode: A new paradigm for one-time training and global prediction in multilingual sentiment analysis

Negation of German polar words and expressions in automated analysis of text tonality

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