Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data

Detecting sentiments in natural language is tricky even for humans, making its automated detection more complicated. This research proffers a hybrid deep learning model for fine-grained sentiment prediction in real-time multimodal data. It reinforces the strengths of deep learning nets in combination to machine learning to deal with two specific semiotic systems, namely the textual (written text) and visual (still images) and their combination within the online content using decision level multimodal fusion. The proposed contextual ConvNet-SVMBoVW model, has four modules, namely, the discretization, text analytics, image analytics, and decision module. The input to the model is multimodal text, m ε {text, image, info-graphic}. The discretization module uses Google Lens to separate the text from the image, which is then processed as discrete entities and sent to the respective text analytics and image analytics modules. Text analytics module determines the sentiment using a hybrid of a convolution neural network (ConvNet) enriched with the contextual semantics of SentiCircle. An aggregation scheme is introduced to compute the hybrid polarity. A support vector machine (SVM) classifier trained using bag-of-visual-words (BoVW) for predicting the visual content sentiment. A Boolean decision module with a logical OR operation is augmented to the architecture which validates and categorizes the output on the basis of five fine-grained sentiment categories (truth values), namely ‘highly positive,’ ‘positive,’ ‘neutral,’ ‘negative’ and ‘highly negative.’ The accuracy achieved by the proposed model is nearly 91% which is an improvement over the accuracy obtained by the text and image modules individually

Анализ тональности текста методами машинного обучения

В данной обзорной статье описаны основные методы используемые для анализа тональности текста. Представлены методы традиционного машинного обучения, используемые для анализа тональности, а также описаны методы глубокого обучени

HindiPersonalityNet: Personality Detection in Hindi Conversational Data using Deep Learning with Static Embedding

Personality detection along with other behavioural and cognitive assessment can essentially explain why people act the way they do and can be useful to various online applications such as recommender systems, job screening, matchmaking, and counselling. Additionally, psychometric NLP relying on textual cues and distinctive markers in writing style within conversational utterances reveal signs of individual personalities. This work demonstrates a text-based deep neural model, HindiPersonalityNet of classifying conversations into three personality categories {ambivert, extrovert, introvert} for detecting personality in Hindi conversational data. The model utilizes GRU with BioWordVec embeddings for text classification and is trained/tested on a novel dataset, शख्सियत (pronounced as Shakhsiyat) curated using dialogues from an Indian crime-thriller drama series, Aarya. The model achieves an F1-score of 0.701 and shows the potential for leveraging conversational data from various sources to understand and predict a person's personality traits. It exhibits the ability to capture semantic as well as long-distance dependencies in conversations and establishes the effectiveness of our dataset as a benchmark for personality detection in Hindi dialogue data. Further, a comprehensive comparison of various static and dynamic word embedding is done on our standardized dataset to ascertain the most suitable embedding method for personality detection

Sentiment Analysis of Text Memes: A Comparison Among Supervised Machine Learning Methods

Meme is a new form of content in social media. A meme contains sentiment towards a particular issue, product, person, or entity. Memes can be in the form of text, images, or images that contain text. Memes are entertaining, critical, sarcastic, and may even be political. Traditional sentiment analysis methods deal with text. This study compares the performance of four sentiment analysis methods when used on Indonesian meme in the form of text and images that contain text. Firstly, the extraction of text memes was carried out, followed by the classification of the extracted text memes using supervised machine learning methods, namely Naïve Bayes, Support Vector Machines, Decision Tree, and Convolutional Neural Networks. Based on the experimental results, sentiment analysis on meme text using the Naïve Bayes method produced the best results, with an accuracy of 65.4%

Knowledge-based Data Processing for Multilingual Natural Language Analysis

Natural Language Processing (NLP) aids the empowerment of intelligent machines by enhancing human language understanding for linguistic-based human-computer communication. Recent developments in processing power, as well as the availability of large volumes of linguistic data, have enhanced the demand for data-driven methods for automatic semantic analysis. This paper proposes multilingual data processing using feature extraction with classification using deep learning architectures. Here, the input text data has been collected based on various languages and processed to remove missing values and null values. The processed data has been extracted using Histogram Equalization based Global Local Entropy (HEGLE) and classified using Kernel-based Radial basis Function (Ker_Rad_BF). These architectures could be utilized to process natural language. We present solutions to the multilingual sentiment analysis issue in this research article by implementing algorithms, and we compare precision factors to discover the optimum option for multilingual sentiment analysis. For the HASOC dataset, the proposed HEGLE_ Ker_Rad_BF achieved an accuracy of 98%, a precision of 97%, a recall of 90.5%, an f-1 score of 85%, RMSE of 55.6% and a loss curve analysis attained 44%. For the TRAC dataset, the accuracy of 98%, the precision attained is 97%, the Recall is 91%, the F-1 score is 87%, and the RMSE of the proposed neural network is 55%

DomainSenticNet: An Ontology and a Methodology Enabling Domain-aware Sentic Computing

[EN] In recent years, SenticNet and OntoSenticNet have represented important developments in the novel interdisciplinary field of research known as sentic computing, enabling the development of a variety of Sentic applications. In this paper, we propose an extension of the OntoSenticNet ontology, named DomainSenticNet, and contribute an unsupervised methodology to support the development of domain-aware Sentic applications. We developed an unsupervised methodology that, for each concept in OntoSenticNet, mines semantically related concepts from WordNet and Probase knowledge bases and computes domain distributional information from the entire collection of Kickstarter domain-specific crowdfunding campaigns. Subsequently, we applied DomainSenticNet to a prototype tool for Kickstarter campaign authoring and success prediction, demonstrating an improvement in the interpretability of sentiment intensities. DomainSenticNet is an extension of the OntoSenticNet ontology that integrates each of the 100,000 concepts included in OntoSenticNet with a set of semantically related concepts and domain distributional information. The defined unsupervised methodology is highly replicable and can be easily adapted to build similar domain-aware resources from different domain corpora and external knowledge bases. Used in combination with OntoSenticNet, DomainSenticNet may favor the development of novel hybrid aspect-based sentiment analysis systems and support further research on sentic computing in domain-aware applications.The work of Paolo Rosso was partially funded by the Spanish MICINN under the project PGC2018-096212-B-C31.Distante, D.; Faralli, S.; Rittinghaus, S.; Rosso, P.; Samsami, N. (2022). DomainSenticNet: An Ontology and a Methodology Enabling Domain-aware Sentic Computing. Cognitive Computation. 14(1):62-77. https://doi.org/10.1007/s12559-021-09825-w627714

Submission of Written Evidence to the House of Lords Communications and Digital Committee Inquiry on The Future of News: Impartiality, Trust, and Technology

I. Technology's Dual Impact on News: Technology and AI democratize information access and innovate news delivery, but also bring challenges like major platform dominance and misinformation, necessitating a balance for a diverse news environment. II. Influence of Major Platforms: The large technology platforms are the key shapers of public opinion and news visibility, these platforms enhance information access yet can overshadow smaller outlets, significantly impacting news dynamics play a key role in shaping public opinion and determining the visibility of news content. III. Generative AI's Role in Media Business: Generative AI’s integration into newsrooms facilitates automated content creation and enhances user-specific content delivery, reshaping journalism's traditional models. However, it also presents challenges like ensuring authenticity and managing AI-generated misinformation, necessitating a balance between technological innovation and ethical journalistic practices. IV. Evolving Perceptions of Impartiality: A growing complexity in maintaining impartiality amidst societal polarization; news outlets must balance unbiased reporting with diverse audience expectations. Media and AI literacy is key to ensuring a well-informed public capable of critically engaging with news in the digital age. V. Effectiveness of Regulatory Oversight: As the media environment becomes increasingly complex with the advent of digital platforms, the role of regulators like Ofcom is crucial but also challenged. This situation calls for a potential reassessment and adaptation of regulatory approaches to address the nuances of modern media consumption and distribution more effectively. VI. Government's Intervention in Media: Government intervention in media can play a pivotal role in ensuring news impartiality and trust, dealing with challenges like the influence of major tech platforms, media plurality, and misinformation. While being vital for a balanced media environment, it is important to preserve journalistic independence and prevent excessive governmental influence, ensuring a healthy, diverse, and independent media landscape. VII. Rumour Control and Influential Nodes: AI literacy and efficient rumour control are key to maintaining news integrity and building trust while addressing impartiality issues. This involves using AI-driven models for rumour management and techniques to identify influential individuals and nodes within social networks that can maximize news virality or trust. These strategies are fundamental in navigating the complexities of information dissemination and control in the digital media landscape

Multimodal Sentiment Analysis Based on Deep Learning: Recent Progress

Multimodal sentiment analysis is an important research topic in the field of NLP, aiming to analyze speakers\u27 sentiment tendencies through features extracted from textual, visual, and acoustic modalities. Its main methods are based on machine learning and deep learning. Machine learning-based methods rely heavily on labeled data. But deep learning-based methods can overcome this shortcoming and capture the in-depth semantic information and modal characteristics of the data, as well as the interactive information between multimodal data. In this paper, we survey the deep learning-based methods, including fusion of text and image and fusion of text, image, audio, and video. Specifically, we discuss the main problems of these methods and the future directions. Finally, we review the work of multimodal sentiment analysis in conversation

Multimodal cyberbullying detection using capsule network with dynamic routing and deep convolutional neural network

Cyberbullying is the use of information technology networks by individuals’ to humiliate, tease, embarrass, taunt, defame and disparage a target without any face-to-face contact. Social media is the 'virtual playground' used by bullies with the upsurge of social networking sites such as Facebook, Instagram, YouTube and Twitter. It is critical to implement models and systems for automatic detection and resolution of bullying content available online as the ramifications can lead to a societal epidemic. This paper presents a deep neural model for cyberbullying detection in three different modalities of social data, namely textual, visual and info-graphic (text embedded along with an image). The all-in-one architecture, CapsNet–ConvNet, consists of a capsule network (CapsNet) deep neural network with dynamic routing for predicting the textual bullying content and a convolution neural network (ConvNet) for predicting the visual bullying content. The info-graphic content is discretized by separating text from the image using Google Lens of Google Photos app. The perceptron-based decision-level late fusion strategy for multimodal learning is used to dynamically combine the predictions of discrete modalities and output the final category as bullying or non-bullying type. Experimental evaluation is done on a mix-modal dataset which contains 10,000 comments and posts scrapped from YouTube, Instagram and Twitter. The proposed model achieves a superlative performance with the AUC–ROC of 0.98

Systematic Literature Review: Analisis Sentimen Berbasis Deep Learning

Systematic literature review ini bertujuan untuk mengetahui tren penelitian analisis sentimen berbasis deep learning antara tahun 2020-2023. Fokus kajiannya adalah pada pemahaman tentang pemodelan yang digunakan oleh banyak peneliti, juga nilai akurasi dari masing-masing klasifikasi tersebut. Pertanyaan utama dalam SLR ini yaitu teknik analisis sentimen berbasis deep learning apa yang memberikan akurasi tertinggi. Peneliti menemukan 400 artikel terindeks Scopus dengan menggunakan Publish or Perish 8. Selanjutnya, penyaringan jurnal dan pencarian kluster menggunakan aplikasi Microsoft Excel, Zotero, Mendeley, dan VOS Viewer yang menghasilkan 105 artikel terpilih untuk dianalisis secara deskriptif. Berdasarkan hasil temuan metode yang populer digunakan dalam melakukan analisis sentimen berbasis deep learning dalam jangka waktu yang telah ditentukan adalah metode LSTM dan CNN, baik dilakukan satu metode maupun keduanya. Adapun akurasi tertinggi mencapai 99% dengan rata-rata 89% menggunakan metode LSTM. Pengetahuan ini dapat digunakan untuk mengusulkan model analisis sentimen berbasis deep learning yang memberikan akurasi tertinggi