Sentiment Analysis of Short Texts Using SVMs and VSMs-Based Multiclass Semantic Classification

Suresh Kumar, K.; Radha Mani, A.S.; Ananth Kumar, T.; Jalili, Ahmad; Gheisari, Mehdi; Malik, Yasir; Chen, Hsing-Chung; Jahangir Moshayedi, Ata

doi:10.1080/08839514.2024.2321555

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…This consolidated feature matrix serves as the input to a machine learning pipeline, at the heart of which lies a Support Vector Classifier (SVC), with a grid search for optimal kernel (linear, polynomial, sigmoid, RBF) and hyper parameters (cost C and γ when relevant). This choice of classifier aligns with the high-dimensional nature of the feature space and is well-regarded for text classification tasks in recent articles and surveys (HaCohen-Kerner, 2022;Bevendorff et al, 2023;Fauzi et al, 2023), and particularly fit in the case of shorter texts Camps, 2021, 2023;Vogel and Meghana, 2021;Suresh Kumar et al, 2024).…”

Section: Feature Extractionmentioning

confidence: 96%

Harnessing Linguistic Analysis for ADHD Diagnosis Support: A Stylometric Approach to Self-Defining Memories

Cafiero,

Rudloff,

Gabay

et al. 2024

Preprint

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This study explores the potential of stylometric analysis in identifying Self-Defining Memories (SDMs) authored by individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) versus a control group. A sample of 198 SDMs were written by 66 adolescents and were then analysed using Support Vector Classifiers (SVC). The analysis included a variety of linguistic features such as character 3-grams, function words, sentence length, or lexical richness among others. It also included metadata about the participants (gender, age) and their SDMs (self-reported sentiment after recalling their memories). The results reveal a promising ability of linguistic analysis to accurately classify SDMs, with perfect prediction (F1=1.0) in the contextually simpler setup of text-by-text prediction, and satisfactory levels of precision (F1 = 0.77) when predicting individual by individual. Such results highlight the significant role that linguistic characteristics play in reflecting the distinctive cognitive patterns associated with ADHD. While not a substitute for professional diagnosis, textual analysis offers a supportive avenue for early detection and a deeper understanding of ADHD.

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Section: Feature Extractionmentioning

confidence: 96%

Harnessing Linguistic Analysis for ADHD Diagnosis Support: A Stylometric Approach to Self-Defining Memories

Cafiero,

Rudloff,

Gabay

et al. 2024

Preprint

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Quantum theory-inspired inter-sentence semantic interaction model for textual adversarial defense

Huang,

Chen,

et al. 2024

Complex Intell. Syst.

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Deep neural networks have a recognized susceptibility to diverse forms of adversarial attacks in the field of natural language processing and such a security issue poses substantial security risks and erodes trust in artificial intelligence applications among people who use them. Meanwhile, quantum theory-inspired models that represent word composition as a quantum mixture of words have modeled the non-linear semantic interaction. However, modeling without considering the non-linear semantic interaction between sentences in the current literature does not exploit the potential of the quantum probabilistic description for improving the robustness in adversarial settings. In the present study, a novel quantum theory-inspired inter-sentence semantic interaction model is proposed for enhancing adversarial robustness via fusing contextual semantics. More specifically, it is analyzed why humans are able to understand textual adversarial examples, and a crucial point is observed that humans are adept at associating information from the context to comprehend a paragraph. Guided by this insight, the input text is segmented into subsentences, with the model simulating contextual comprehension by representing each subsentence as a particle within a mixture system, utilizing a density matrix to model inter-sentence interactions. A loss function integrating cross-entropy and orthogonality losses is employed to encourage the orthogonality of measurement states. Comprehensive experiments are conducted to validate the efficacy of proposed methodology, and the results underscore its superiority over baseline models even commercial applications based on large language models in terms of accuracy across diverse adversarial attack scenarios, showing the potential of proposed approach in enhancing the robustness of neural networks under adversarial attacks.

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Effective text classification using BERT, MTM LSTM, and DT

Jamshidi,

Mohammadi,

Bagheri

et al. 2024

Data & Knowledge Engineering

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Sentiment Analysis of Short Texts Using SVMs and VSMs-Based Multiclass Semantic Classification

Cited by 3 publications

References 30 publications

Harnessing Linguistic Analysis for ADHD Diagnosis Support: A Stylometric Approach to Self-Defining Memories

Harnessing Linguistic Analysis for ADHD Diagnosis Support: A Stylometric Approach to Self-Defining Memories

Quantum theory-inspired inter-sentence semantic interaction model for textual adversarial defense

Effective text classification using BERT, MTM LSTM, and DT

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