scholarly journals Improving the Accuracy in Sentiment Classification in the Light of Modelling the Latent Semantic Relations

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 307 ◽  
Author(s):  
Nina Rizun ◽  
Yurii Taranenko ◽  
Wojciech Waloszek
Keyword(s):  
Probabilistic Models ◽  
Complex Structure ◽  
Sentiment Classification ◽  
Probabilistic Methods ◽  
Semantic Relations ◽  
Semantic Structure ◽  
Scientific Contribution ◽  
Context Sensitive ◽  
Accuracy Level ◽  
Sentiment Dictionary

The research presents the methodology of improving the accuracy in sentiment classification in the light of modelling the latent semantic relations (LSR). The objective of this methodology is to find ways of eliminating the limitations of the discriminant and probabilistic methods for LSR revealing and customizing the sentiment classification process (SCP) to the more accurate recognition of text tonality. This objective was achieved by providing the possibility of the joint usage of the following methods: (1) retrieval and recognition of the hierarchical semantic structure of the text and (2) development of the hierarchical contextually-oriented sentiment dictionary in order to perform the context-sensitive SCP. The main scientific contribution of this research is the set of the following approaches: at the phase of LSR revealing (1) combination of the discriminant and probabilistic models while applying the rules of adjustments to obtain the final joint result; at all SCP phases (2) considering document as a complex structure of topically completed textual components (paragraphs) and (3) taking into account the features of persuasive documents’ type. The experimental results have demonstrated the enhancement of the SCP accuracy, namely significant increase of average values of recall and precision indicators and guarantee of sufficient accuracy level.

scholarly journals Improving the Accuracy in Text Classification Methodology in Light of Modelling the Latent Semantic Relations

2018 ◽  
Author(s):  
Nina Rizun ◽  
Yurii Taranenko ◽  
Wojciech Waloszek
Keyword(s):  
Text Classification ◽  
Subjective Evaluation ◽  
Complex Structure ◽  
Probabilistic Methods ◽  
Semantic Relations ◽  
Scientific Contribution ◽  
Sentiment Dictionary ◽  
Process Combination ◽  
Qualitative Characteristics

The research presents the Methodology of Improving the Accuracy in Text Classification in Light of Modelling the Latent Semantic Relations (LSR). The aim of this Methodology is to find the ways of eliminating the Limitations of Discriminant and Probabilistic methods for LSR revealing and customizing the Text Classification Process to the more accurate recognition of the text tonality. This aim should be achieved by using the knowledge about the text’s Hierarchical Semantic Context in the form of Corpora-based Hierarchical Sentiment Dictionary. The main scientific contribution of this research is the following set of approaches to improve the qualitative characteristics of Text Classification process: combination of the Discriminant and Probabilistic methods allowing to decrease the influences of the Limitations of these methods on the LSR revealing process; considering each document as a complex structure allowing to estimate documents integrally by separated classification of topically completed textual component (paragraphs); taking into account the features of Argumentative type of documents (Reviews) allowing to use the author’s subjective evaluation of text tonality for development the Text Classification methodology. Tonality, expressed by the Review’s author, has a significant, but not critical, effect on the qualitative indicators of Sentiment Recognition.

Comparison of Probabilistic and Possibility Theory-Based Methods for Design Against Catastrophic Failure Under Uncertainty

1999 ◽  
Author(s):  
Efstratios Nikolaidis ◽  
Harley Cudney ◽  
Sophie Chen ◽  
Raphael T. Haftka ◽  
Raluca Rosca
Keyword(s):  
Probabilistic Models ◽  
Failure Modes ◽  
Possibility Theory ◽  
Probabilistic Methods ◽  
Sufficient Information ◽  
Catastrophic Failure ◽  
Design Problems ◽  
Available Information ◽  
Theoretical Foundations ◽  
Better Than

Abstract This paper compares probabilistic and possibility-based methods for design against catastrophic failure under uncertainty. It studies the effect of the amount of information on the effectiveness of each method. The study is confined to problems where the boundary between survival and failure is sharp. First, the paper examines the theoretical foundations of probability and possibility. It also compares the two methods when they are used to assess the risk of a system. Finally, it compares the two methods on two design problems. A major difference between probability and possibility is in the axioms about the union of events. Because of this difference, probability and possibility calculi are fundamentally different and one cannot simulate possibility calculus using probabilistic models. It is shown that possibility-based methods can be less conservative than probability-based methods in systems with many failure modes. On the other hand, possibility-based methods tend to be more conservative than probability-based methods in systems that fail only if many unfavorable events occur simultaneously. Probabilistic methods are better than possibility-based methods if sufficient information is available. However, the latter can be better if little information is available. A principal reason is that it is easier to identify the most conservative possibilistic model than the most conservative probabilistic model that is consistent with the available information.

On twittizens and city residents

2020 ◽  
Vol 15 (1) ◽  
pp. 21-41
Author(s):  
Elizaveta Tarasova ◽  
Natalia Beliaeva
Keyword(s):  
Formation Mechanism ◽  
Native Speakers ◽  
Native Speaker ◽  
Word Formation ◽  
Semantic Relations ◽  
Semantic Structure ◽  
Web Based ◽  
Semantic Level ◽  
Semantic Relationships ◽  
Experimental Findings

Abstract The present study analyses native speaker perceptions of the differences in the semantic structure of compounds and blends to specify whether the formal differences between compounds and blends are reflected on the semantic level. Viewpoints on blending vary, with some researchers considering it to be an instance of compounding (Kubozono, 1990), while others identify blending as an interim word formation mechanism between compounding and shortening (López Rúa, 2004). The semantic characteristics of English determinative blends and N+N subordinative compounds are compared by evaluating the differences in native speakers’ perceptions of the semantic relationships between constituents of the analysed structures. The results of two web-based experiments demonstrate that readers’ interpretations of both compounds and blends differ in terms of lexical indicators of semantic relations between the elements of these units. The experimental findings indicate that language users’ interpretation of both compounds and blends includes information on semantic relationships. The differences in the effect of the semantic relations on interpretations is likely to be connected to the degree of formal transparency of these units.

Vehicle Risk Level Estimation by Using Experimental Trajectories in Bend

2011 ◽  
Author(s):  
Abdourahmane Koita ◽  
Dimitri Daucher ◽  
Michel Fogli
Keyword(s):  
Probability Density ◽  
Reliability Analysis ◽  
Probabilistic Models ◽  
Hermite Polynomials ◽  
Probabilistic Methods ◽  
Risk Level ◽  
General Context ◽  
Risk Levels ◽  
Modelling Uncertainties ◽  
Non Gaussian

This paper tackles the general context of road safety, focussing on the light vehicles safety in bends. It consists to use a reliability analysis in order to estimate the failure probability of vehicle trajectories. Firstly, we build probabilistic models able to describe measured trajectories in a given bend. The models are transforms of scalar normalized second order stochastic processes which are stationary, ergodic and non-Gaussian. The process is characterized by its probability density function and its power spectral density estimated starting from the experimental trajectories. The probability density is approximated by a development on the Hermite polynomials basis. The second part is devoted to apply a reliability strategy intended to associate a risk level to each class of trajectories. Based on the joint use of probabilistic methods for modelling uncertainties, reliability analysis for assessing risk levels and statistics for classifying the trajectories, this approach provides a realistic answer to the tackled problem.

scholarly journals Structural and Semantic Peculiarities of Verbal Derivatives of Onomasiological Categories

2020 ◽  
pp. 175-185
Author(s):  
Olga Migorian ◽  
Tetiana Pavlovych
Keyword(s):  
English Language ◽  
Word Formation ◽  
English Language Development ◽  
Semantic Relations ◽  
Semantic Structure ◽  
Main Task ◽  
Structural Units ◽  
Lexical Unit ◽  
Lexical Study ◽  
Derivatives Of

During the last century, the development of word-forming issues has been investigated so intensely that today it is possible to state the existance of a number of approaches and its versatile study both in synchrony and diachrony. Some linguists have studied the issues of word formation within etymology, while others have considered the problems of word formation in the context of grammar, focusing on structural analysis. Representatives of the lexical study described predominantly semantic relations between different structural units. Confirmation of the theory of interaction of different linguistic levels was the study of structural and semantic relations in oppositional pairs of "forming lexical unit – derivative". The main task of historical and onomasiological research, which is the basis of our research is to reveal the nature of the semantic structure of the concept; to trace the basic tendencies of the historical development of the prefixal way of word formation in English, the change of its semantic boundaries and the basic structures from epoch to epoch. The linguistic form of content is a word in general and a derivative in particular. The article presents an attempt to investigate the dynamics of efficiency of structural and semantic patterns of verbal prefixal derivatives within onomasiological categories during four periods of the English language development.

scholarly journals NOMINATIVE FIELD OF THE CONCEPT MARRIAGE AND ANALYSIS OF THE SYNONYMOUS RANGE OF ITS KEY UNIT

2021 ◽  
Vol 1 (193) ◽  
pp. 218-224
Author(s):  
Nadiya Ivanenko ◽  
Keyword(s):  
Cognitive Linguistics ◽  
English Language ◽  
Complex Structure ◽  
Field Structure ◽  
Semantic Structure ◽  
Semantic Field ◽  
Lexical Semantic ◽  
The World ◽  
Basic Characteristics ◽  
Dictionary Definitions

The research focuses on the study of the actualization of the concept MARRIAGE in the context of the linguocognitive and linguocultural paradigm. The article analyzes the means of modeling the concept MARRIAGE in the British language picture of the world, its content, structure and cognitive interpretation. The concept-cognitive MARRIAGE is considered in the direction of anthropocentrism with consideration of modern achievements of cognitive linguistics, and also the place of this concept in construction of the British national picture of the world is defined. In the English language tradition, this social phenomenon is expressed through the lexical-semantic field of the concept MARRIAGE. The composition of other basic concepts of linguistic consciousness largely depends on the concept MARRIAGE. The article presents the results of etymological analysis. It plays a big role in determining the typology of culture and the need for this analysis helps to establish the source of origin of the conceptualizer. The analysis of dictionary definitions made it possible to investigate all the meanings of lexical units of the outlined nominative field. This allowed us to understand the nature and types of semantic structure of words that belong to different semantic groups and semasiological subclasses, as well as to look at the epidemiological relations of the key. In order to describe the complex structure of the organization of a multi-valued keyword, the notion of lexical-semantic variant is used. Basic characteristics of the concept MARRIAGE are possible to be found in the dictionary definitions and the complex structure of the concept is defined as a field structure, that is: denotative central content with semantic nucleus, peripherality and connotative surrounding.

Context-sensitive and attribute-based sentiment classification of online consumer-generated content

Kybernetes ◽  
2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Barkha Bansal ◽  
Sangeet Srivastava
Keyword(s):  
Latent Dirichlet Allocation ◽  
Brand Management ◽  
Sentiment Classification ◽  
Practical Significance ◽  
Sensitive Information ◽  
Content Type ◽  
Context Sensitive ◽  
Online Consumer ◽  
Product Improvement

Purpose Vast volumes of rich online consumer-generated content (CGC) can be used effectively to gain important insights for decision-making, product improvement and brand management. Recently, many studies have proposed semi-supervised aspect-based sentiment classification of unstructured CGC. However, most of the existing CGC mining methods rely on explicitly detecting aspect-based sentiments and overlooking the context of sentiment-bearing words. Therefore, this study aims to extract implicit context-sensitive sentiment, and handle slangs, ambiguous, informal and special words used in CGC. Design/methodology/approach A novel text mining framework is proposed to detect and evaluate implicit semantic word relations and context. First, POS (part of speech) tagging is used for detecting aspect descriptions and sentiment-bearing words. Then, LDA (latent Dirichlet allocation) is used to group similar aspects together and to form an attribute. Semantically and contextually similar words are found using the skip-gram model for distributed word vectorisation. Finally, to find context-sensitive sentiment of each attribute, cosine similarity is used along with a set of positive and negative seed words. Findings Experimental results using more than 400,000 Amazon mobile phone reviews showed that the proposed method efficiently found product attributes and corresponding context-aware sentiments. This method also outperforms the classification accuracy of the baseline model and state-of-the-art techniques using context-sensitive information on data sets from two different domains. Practical implications Extracted attributes can be easily classified into consumer issues and brand merits. A brand-based comparative study is presented to demonstrate the practical significance of the proposed approach. Originality/value This paper presents a novel method for context-sensitive attribute-based sentiment analysis of CGC, which is useful for both brand and product improvement.

Sign in / Sign up

Export Citation Format

Share Document