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\"The primary objective of this book is to build a better and safer social media space by making human language available on different social media platforms intelligible for machines with the blessings of AI. This book bridges the gap between Natural Language Processing (NLP), Advanced Machine(AML) and Deep Learning (DL), and Online Social Media. This book connects various interdisciplinary domains related to Natural Language Understanding, Deep machine Leaning Technology and will be highly beneficial for the students, researchers, and academicians working in this area as this book will cover state-of-the-art technologies around NLP and DML techniques and their role in Social Media Data Analysis. Furthermore, the OSN service providers will take the advantage of this book to update, modify and make better social platforms for its users. Psychiatrists and clinicians will also be beneficial as this book's main focus are to analyze the user behavior in Online Social networks which play a key ingredient in several psychological tests\"-- Provided by publisher.

The objective of the article is twofold: first, to employ the knowledge of the recurrence of low-frequency words in authorial texts; and second, to prevent the misuse of this knowledge. Contrary to the prevailing authorship attribution theory and practice (Evert et al. 2017, Juola 2008), our research has revealed that the personal linguistic profile is not primarily composed of frequent words with grammatical functions. Instead, we have identified that a distinct set of full-meaning words defines an individual’s linguistic profile (Faltýnek 2020, Faltýnek – Matlach 2021). An examination of these meanings reveals an individual’s unconscious language habits and, consequently, their personality settings. Such personal profiling is referred to as “deep content” and “deep sentiment analysis”. The innovation in question has the potential to facilitate a novel form of linguistic personalization in digital communication, one that has not been previously observed or utilized. The main aim of this article is to describe the algorithm to conduct single-person linguistic deep content and deep sentiment profiling and personalization. We will describe technical steps to provide such a form of digital communication processing and to facilitate the adjustment of a text targeted at an individual, described as a System and method for adapting text based data structures to text samples (Patent No.: US11797753B2, Faltýnek et al. 2023). This algorithm can be used to (a) produce a personal linguistic profile (analogically to psychometrics instruments such as NEO-FFI Big Five, Minnesota Multiphasic Personality Inventory (MMPI)), (b) target digital communication to an individual by “translating” a text to their language (i.e. linguistic habits) and stimulate desired feelings to a predetermined content. The algorithm is, however, also designed (c) to be used to avoid procedures (a) and (b) using any kind of digital communication platform by an individual. This algorithm is implemented in the software Cloakspeech (Faltýnek – Benešová – Kučera 2025), which provides personalization of AI-generated texts: AI speaks like a particular person.

In the world of social media, the amount of textual data is increasing exponentially on the internet, and a large portion of it expresses subjective opinions. Sentiment Analysis (SA) also named as Opinion mining, which is used to automatically identify and extract the subjective sentiments from text. In recent years, the research on sentiment analysis started taking off because of a huge of amount of data is available on the social media like twitter, machine learning algorithms popularity is increased in IR (Information Retrieval) and NLP (Natural Language Processing). In this work, we proposed three phase systems for sentiment classification in twitter tweets task of SemEval competition. The task is predicting the sentiment like negative, positive or neutral of a twitter tweets by analyzing the whole tweet. The first system used Artificial Bee Colony (ABC) optimization technique is used with Bag-of-words (BoW) technique in association with Naive Bayes (NB) and k-Nearest Neighbor (kNN) classification techniques with combination of various categories of features in identifying the sentiment for a given twitter tweet. The second system used to preserve the context a Rider Feedback Artificial Tree Optimization-enabled Deep Recurrent neural networks (RFATO-enabled Deep RNN) is developed for the efficient classification of sentiments into various grades. Further to improve the accuracy of classification on n-valued scale Adaptive Rider Feedback Artificial Tree (Adaptive RiFArT)-based Deep Neuro fuzzy network is devised for efficient sentiment grade classification. Finally, this research work proposed a Fuzzy-Rule Based Deep Sentiment Extraction (FBDSE) Algorithm with Deep Sentiment Score computation. Accuracy measure is considered to test the proposed systems performance. It was observed that the fuzzy-rule based system achieved good accuracy compared with machine learning and deep learning based approaches.

Recently, we had introduced a model for identifying and removal of toxic content from twitter, using an Information Retrieval (IR) model SOIR (Semantic query Optimization-based Information Retrieval). Based on lexical and semantic analysis, SOIR identifies the class labels of tweets. The result demonstrates the superiority of the SOIR model. This model is accurate but social media is a big data problem and a significant amount of time and memory is required. In this paper the deep learning technique is used to process large-scale social media text data. First uses Natural Language Processing (NLP) based feature extraction to create four different sets of training samples i.e. TF-IDF-based features, POS Tagged Features, a reduced feature vector of POS and the combined vector of TF-IDF and POS tagged features. The deep Convolutional Neural Networks (CNN) is used to train the model and to classify hate and offensive language. The dataset has been obtained from Kaggle. The performance in terms of training accuracy, validation accuracy, training loss and validation loss has been measured with the time complexity. In addition, the class-wise Precision, Recall, F1-score, and Mean accuracy have also been investigated. From experimental results, we found TF-IDF and POS-based combined features provide superior performance.

This paper describes two different approaches to sentiment analysis. The first is a form of symbolic approach that exploits a sentiment lexicon together with a set of shifter patterns and rules. The sentiment lexicon includes single words (unigrams) and is developed automatically by exploiting labeled examples. The shifter patterns include intensification, attenuation/downtoning and inversion/reversal and are developed manually. The second approach exploits a deep neural network, which uses a pre-trained language model. Both approaches were applied to texts on economics and finance domains from newspapers in European Portuguese. We show that the symbolic approach achieves virtually the same performance as the deep neural network. In addition, the symbolic approach provides understandable explanations, and the acquired knowledge can be communicated to others. We release the shifter patterns to motivate future research in this direction.

Researching public opinion can help us learn important facts. People may quickly and easily express their thoughts and feelings on any subject using social media, which creates a deluge of unorganized data. Sentiment analysis on social media platforms like Twitter and Facebook has developed into a potent tool for gathering insights into users' perspectives. However, difficulties in interpreting natural language limit the effectiveness and precision of sentiment analysis. This research focuses on developing a social media sentiment analysis (SMSA) framework, incorporating a custom-built emotion thesaurus to enhance the precision of sentiment analysis. It delves into the efficacy of various deep learning algorithms, under different parameter calibrations, for sentiment extraction from social media. The study distinguishes itself by its unique approach towards sentiment dictionary creation and its application to deep learning models. It contributes new insights into sentiment analysis, particularly in social media contexts, showcasing notable advancements over previous methodologies. The results demonstrate improved accuracy and deeper understanding of social media sentiment, opening avenues for future research and applications in diverse fields.

With the popularity of social networking services, people are increasingly inclined to share their opinions and feelings on social networks, leading to the rapid increase in multimodal posts on various platforms. Therefore, multimodal sentiment analysis has become a crucial research field for exploring users’ emotions. The complex and complementary interactions between images and text greatly heighten the difficulty of sentiment analysis. Previous works conducted rough fusion operations and ignored the study for fine fusion features for the sentiment task, which did not obtain sufficient interactive information for sentiment analysis. This paper proposes a global local fusion neural network (GLFN), which comprehensively considers global and local fusion features, aggregating these features to analyze user sentiment. The model first extracts overall fusion features by attention modules as modality-based global features. Then, coarse-to-fine fusion learning is applied to build local fusion features effectively. Specifically, the cross-modal module is used for rough fusion, and fine-grained fusion is applied to capture the interaction information between objects and words. Finally, we integrate all features to achieve a more reliable prediction. Extensive experimental results, comparisons, and visualization of public datasets demonstrate the effectiveness of the proposed model for multimodal sentiment classification.

[Purpose/Significance] In cross-domain natural language processing (NLP) tasks, deep learning models often exhibit performance variations due to texts with distinct domain characteristics, leading to a decline in model generalization capabilities. Text complexity stands out as one of the most explanatory factors influencing model generalization. [Method/Process] This paper presents two innovative contributions. First, a multi-dimensional text complexity calculation framework grounded in systemic functional linguistics theory was constructed. This framework employs a hierarchical quantification approach: at the lexical level, it dynamically identified four types of non-standard expressions - abbreviations, emoticons, internet buzzwords, and alphanumeric mixed words - and calculated a normative score using a non-linear formula. At the sentence level, an innovative inverse fusion enhancement method (IFEM) was proposed, integrating punctuation anomaly density (weight 0.1), colloquial word ratio (weight 0.4), semantic ambiguity (weight 0.2), and sentence length features (weight 0.3), and generating a structural score through modeling of feature synergy and suppression effects along with an adaptive weighting mechanism. Finally, at the corpus level, a weighted fusion output the global corpus complexity assessment. Experimental results demonstrated that this framework successfully quantifies intrinsic differences between domain texts. For instance, the measured complexity of the waimai₁0k dataset reached 0.703, significantly higher than the 0.552 of the ChnSentiCorp_(h)tlₐll dataset, and it accurately captured complexity changes even after internal text reduction and substitution operations. Second, a knowledge base-enhanced dynamic adaptive CNN-BiLSTM model was designed. This model implemented the following innovative mechanisms: 1) The knowledge base adopts a dual mapping architecture of text-label and vector-label, supporting historical experience knowledge loading and real-time error recording; 2) Feature weights were adjusted based on the knowledge base content, such as strengthening positive semantic representations or weakening negative expressions. The model architecture integrated multi-scale CNN convolutional kernels for local feature extraction, bidirectional long short-term memory networks for capturing long-distance dependencies, and an attention mechanism to focus on key information. To validate the effectiveness of the proposed methods, experiments were conducted on four Chinese datasets. [Results/Conclusions] The results indicate that the complexity calculation framework exhibits strong robustness, with complexity fluctuations below 3.3% after a 20% sample reduction, and a maximum complexity increase of 13.8% upon short text data injection. Moreover, the framework effectively quantifies and differentiates text complexities, as evidenced by the 0.703 complexity of the waimai₁0k dataset compared to the 0.552 of the ChnSentiCorp_(h)tlₐll dataset. Additionally, the proposed model demonstrated optimal performance across both the most standardized ChnSentiCorp_(h)tlₐll dataset and the most challenging waimai₁0k dataset (achieving accuracies of 0.923 8 and 0.943 4, respectively), significantly outperforming Transformer and various large language models such as deepseek-v3 and qwen-plus.

Online learning presents a major challenge for learners, namely the diversification of courses and information overload. In response to this issue, recommender systems are widely used. Nowadays, social networks have become a global platform where individuals share a multitude of information. For instance, Twitter is a social network where users exchange messages and interact with various communities. These interactions on social networks have created a new dimension in the field of online learning. In this article, we propose a novel approach that combines sentiment analysis of learners’ reviews on social networks with collaborative filtering methods to provide more personalized and relevant course recommendations. To achieve this, we explored different models to analyze the sentiments of tweets related to online courses. Additionally, we used collaborative filtering based on k-nearest neighbors (KNN). Our results demonstrate that integrating sentiment analysis provides more relevant recommendations. This has also been shown based on the calculation of root mean square error (RMSE) compared to a traditional approach. In this study, we demonstrated that by leveraging this information from social networks like Twitter, online learning platforms can enhance the effectiveness of their course recommendations, tailoring them to each individual learner’s needs.

Background Artificial intelligence (AI) offers significant opportunities to improve the field of implementation science by supporting key activities such as evidence synthesis, contextual analysis, and decision-making to promote the adoption and sustainability of evidence-based practices. This living scoping review aims to: (1) map applications of AI in implementation research and practice; (2) identify evaluation approaches, reported outcomes, and potential risks; and (3) synthesize reported research gaps and opportunities for advancing the use of AI in implementation science. Methods This scoping review will follow the Joanna Briggs Institute (JBI) methodology and the Cochrane guidance for living systematic reviews. A living scoping review is warranted to keep up with the rapid changes in AI and its growing use in implementation science. We will include empirical studies, systematic reviews, grey literature, and policy documents that describe or evaluate applications of AI to support implementation science across the steps of the Knowledge-to-Action (KTA) Model. AI methods and models of interest include machine learning, deep learning, natural language processing, large language models, and related technologies and approaches. A search strategy will be applied to bibliographic databases (MEDLINE, Embase, CINAHL, PsycINFO, IEEE Xplore, Web of Science), relevant journals, conference proceedings, and preprint servers. Two reviewers will independently screen studies and extract data on AI characteristics, specific implementation task according to the KTA Model, evaluation methods, outcome domains, risks, and research gaps. Extracted data will be analyzed descriptively and synthesized narratively using a mapping approach aligned with the KTA Model. Discussion This living review will consolidate the evidence base on how AI is applied across the spectrum of implementation science. It will inform researchers, policymakers, and practitioners seeking to harness AI to improve the adoption, scale-up, and sustainability of evidence-based interventions, while identifying areas for methodological advancement and risk mitigation. Review registration Open Science Framework, May 2025: https://doi.org/10.17605/OSF.IO/2Q5DV