Text Sentiment Analysis Research Articles

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

ABSTRACT In our approach, a hybrid machine learning model is proposed which uses Enhanced Vector Space Model (EVSM) along with Hybrid Support Vector Machine (HSVM) classifier. Initially the social media-based information is retrieved using Enhanced Vector Space Model (EVSM). EVSMs are employed in order to characterize the text content by mapping them into high-dimensional vector spaces, capturing the relationships between words and their contextual meanings. Rigorous feature selection methods are employed to designate texts for review, and a multiclass semantic classification algorithm, specifically the HSVM classifier, is utilized for categorization. Decision tree algorithm is used along with SVM to refine the selection process. To enhance sentiment analysis accuracy, sentiment dictionaries are not only presented but also extended through the expansion of Stanford’s GloVE tool. To enhance precision, the proposed work introduces weight-enhancing methods for processing renowned text weights. Sentiments are classified into positive, negative, and neutral categories. Notably, the achieved results demonstrate improved accuracy, attributed to the incorporation of an emotional sentiment enhancement factor for determining weights and leveraging sentiment dictionaries for word availability. The accuracy is obtained to be 92.78% with 91.33% positive sentiment rate and 97.32% negative sentiment rate.

Research Advanced in Multimodal Emotion Recognition Based on Deep Learning

In summary, the field of computer science has long been intrigued by emotion recognition, which seeks to decode the emotional content hidden within data. Initial approaches to sentiment analysis were predominantly based on single-mode data sources like textual sentiment analysis, speech-based emotion detection, or the study of facial expressions. In recent years, with the increasingly abundant data representations, Many people has gradually pay attention on the multimodal emotion recognition. Multimodal emotion recognition involves not only text, but also audio, image, and video, which is of great significance for enhancing human-computer interaction, improving user experience, and improving emotion-aware applications. This paper thoroughly discusses the research advancements and primary techniques of multimodal emotion recognition tasks, with an emphasis on the aforementioned tasks. Specifically, this paper first introduces representative methods for single-modal emotion recognition based on graphic data, including its basic process, advantages and disadvantages, etc. Secondly, this article introduces pertinent studies on multimodal emotion recognition and offers a quantitative comparison of how different approaches perform on standard multimodal data sets. Lastly, it addresses the complexities inherent in multimodal emotion recognition research and suggests potential areas for future study.

Neural natural language processing for long texts: A survey on classification and summarization

The adoption of Deep Neural Networks (DNNs) has greatly benefited Natural Language Processing (NLP) during the past decade. However, the demands of long document analysis are quite different from those of shorter texts, while the ever increasing size of documents uploaded online renders automated understanding of lengthy texts a critical issue. Relevant applications include automated Web mining, legal document review, medical records analysis, financial reports analysis, contract management, environmental impact assessment, news aggregation, etc. Despite the relatively recent development of efficient algorithms for analyzing long documents, practical tools in this field are currently flourishing. This article serves as an entry point into this dynamic domain and aims to achieve two objectives. First of all, it provides an introductory overview of the relevant neural building blocks, serving as a concise tutorial for the field. Secondly, it offers a brief examination of the current state-of-the-art in two key long document analysis tasks: document classification and document summarization. Sentiment analysis for long texts is also covered, since it is typically treated as a particular case of document classification. Consequently, this article presents an introductory exploration of document-level analysis, addressing the primary challenges, concerns, and existing solutions. Finally, it offers a concise definition of “long text/document”, presents an original overarching taxonomy of common deep neural methods for long document analysis and lists publicly available annotated datasets that can facilitate further research in this area.

A Study on the Emotional Tendency of Aquatic Product Quality and Safety Texts Based on Emotional Dictionaries and Deep Learning

A Study on the Emotional Tendency of Aquatic Product Quality and Safety Texts Based on Emotional Dictionaries and Deep Learning

Evaluating text classification with explainable artificial intelligence

<span lang="EN-US">Nowadays, artificial intelligence (AI) in general and machine learning techniques in particular has been widely employed in automated systems. Increasing complexity of these machine learning based systems have consequently given rise to blackbox models that are typically not understandable or explainable by humans. There is a need to understand the logic and reason behind these automated decision-making black box models as they are involved in our day-to-day activities such as driving, facial recognition identity systems, online recruitment. Explainable artificial intelligence (XAI) is an evolving field that makes it possible for humans to evaluate machine learning models for their correctness, fairness, and reliability. We extend our previous research work and perform a detailed analysis of the model created for text classification and sentiment analysis using a popular Explainable AI tool named local interpretable model agnostic explanations (LIME). The results verify that it is essential to evaluate machine learning models using explainable AI tools as accuracy and other related metrics does not ensure the correctness, fairness, and reliability of the model. We also present the comparison of explainability and interpretability of various machine learning algorithms using LIME. </span>

Optimizer algorithms and convolutional neural networks for text classification

<span lang="EN-US">Lately, deep learning has improved the algorithms and the architectures of several natural language processing (NLP) tasks. In spite of that, the performance of any deep learning model is widely impacted by the used optimizer algorithm; which allows updating the model parameters, finding the optimal weights, and minimizing the value of the loss function. Thus, this paper proposes a new convolutional neural network (CNN) architecture for text classification (TC) and sentiment analysis and uses it with various optimizer algorithms in the literature. Actually, in NLP, and particularly for sentiment classification concerns, the need for more empirical experiments increases the probability of selecting the pertinent optimizer. Hence, we have evaluated various optimizers on three types of text review datasets: small, medium, and large. Thereby, we examined the optimizers regarding the data amount and we have implemented our CNN model on three different sentiment analysis datasets so as to binary label text reviews. The experimental results illustrate that the adaptive optimization algorithms Adam and root mean square propagation (RMSprop) have surpassed the other optimizers. Moreover, our best CNN model which employed the RMSprop optimizer has achieved 90.48% accuracy and surpassed the state-of-the-art CNN models for binary sentiment classification problems.</span>

Punctuation and lexicon aid representation: A hybrid model for short text sentiment analysis on social media platform

Sentiment analysis measures user experience on social media. With the emergence of pre-trained models, text classification tasks have become homogeneous, without a significant improvement in accuracy. Therefore, we present a hybrid model called PLASA to classify the sentiment polarity of short comments, particularly barrages. PLASA introduces a collaborative attention module that integrates information about relative position and knowledge from summarized lexicons to better adjust the relationship between word representations. Our model is evaluated using three new curated sentiment analysis datasets: SentiTikTok-2023 (4613 items), SentiBilibili-2023 (7755 items), and SentiWeibo-2023 (5614 items). Although the comment length varies across datasets, all maintain a consistent punctuation percentage at approximately 13%. Consequently, PLASA with the optimal combination demonstrates notable performance improvements compared to both the baseline and commonly used models. It achieves micro-F1 scores of 93.94%, 90.34%, and 88.79% on the respective datasets. We also observed that the representation capacity of the pre-trained model decreases as the text length increases. Moreover, the proposed collaborative attention module effectively addresses this limitation, as confirmed by our ablation study.

Sentiment Analysis of Code-Mixed Text: A Comprehensive Review

Sentiment Analysis is the task of identifying and extracting the opinion expressed in a text to determine the writer's perception of an entity. Due to globalization, people often mix two or more languages and use phonetic typing and lexical borrowing in web communication. This concept is known as code-mixing. Although extracting the opinion of text written in monolingual languages is simple and straightforward, Sentiment Analysis of code-mixed text is challenging. Classifiers fail within the context of the code-mixed text as text may consist of creative writing, spelling variations, grammatical errors, and different word orders. Hence, SA of code-mixed text is an interesting, challenging, and popular research area. This paper presents the state-of-the-art in Sentiment Analysis of code-mixed text by discussing each concept in detail. The paper also discusses the focused areas, techniques used, limitations, and performances of the studies related to code-mixing.

Interpretability of deep learning models in analysis of Spanish financial text

AbstractArtificial intelligence methods based on deep learning (DL) have recently made significant progress in many different areas including free text classification and sentiment analysis. We believe that corporate governance is one of these areas, where DL can generate very valuable and differential knowledge, for example, by analyzing the biographies of independent directors, which allows for qualitative modeling of their profile in an automatic way. For this technology to be accepted it is important to be able to explain how it generates its results. In this work we have developed a six-dimensional labeled dataset of independent director biographies, implemented three recurrent DL models based on LSTM and transformers along with four ensembles, one of which is an innovative proposal based on a multi-layer perceptron (MLP), trained them using Spanish language and economics and finance terminology and performed a comprehensive test study that demonstrates the accuracy of the results. We have also performed a complete study of explainability using the SHAP methodology by comparatively analyzing the developed models. We have achieved a mean error (MAE) of 8% in the modeling of the open text biographies, which has allowed us to perform a case study of time analysis that has detected significant variations in the composition of the Standard Expertise Profile (SEP) of the boards of directors, related to the crisis of the period 2008–2013. This work shows that DL technology can be accurately applied to free text analysis in the finance and economic domain, by automatically analyzing large volumes of data to generate knowledge that would have been unattainable by other means.

Text sentiment analysis based on the LSTM method

Sentiment analysis is an important research direction in the field of natural language processing. With the rise of various social network platforms, a large number of network users have begun to express and publish their opinions on X, LINE, Weibo, and other platforms, which makes sentiment analysis more and more important. However, as more and more people use social platforms, the number of comments from Internet users has surged, and Internet buzzwords and Internet popular topics are constantly changing. This phenomenon leads to an increase in the error rate of traditional text emotion detection methods, which in turn leads to a reduction in the operating efficiency of some network platforms. In addition, nowadays, sentiment analysis has a wider and more urgent need in many fields, especially in the business field. In order to cope with this change and demand, this paper focuses on a text sentiment analysis model based on a neural network model.

Negative affect variability differs between anxiety and depression on social media.

Negative affect variability is associated with increased symptoms of internalizing psychopathology (i.e., depression, anxiety). The Contrast Avoidance Model (CAM) suggests that individuals with anxiety avoid negative emotional shifts by maintaining pathological worry. Recent evidence also suggests that the CAM can be applied to major depression and social phobia, both characterized by negative affect changes. Here, we compare negative affect variability between individuals with a variety of anxiety and depression diagnoses by measuring the levels and degree of change in the sentiment of their online communications. Participants were 1,853 individuals on Twitter who reported that they had been clinically diagnosed with an anxiety disorder (A cohort, n = 896) or a depressive disorder (D cohort, n = 957). Mean negative affect (NA) and negative affect variability were calculated using the Valence Aware Dictionary for Sentiment Reasoning (VADER), an accurate sentiment analysis tool that scores text in terms of its negative affect content. Findings showed differences in negative affect variability between the D and A cohort, with higher levels of NA variability in the D cohort than the A cohort, U = 367210, p < .001, r = 0.14, d = 0.25. Furthermore, we found that A and D cohorts had different average NA, with the D cohort showing higher NA overall, U = 377368, p < .001, r = 0.12, d = 0.21. Our sample is limited to individuals who disclosed their diagnoses online, which may involve bias due to self-selection and stigma. Our sentiment analysis of online text may not completely capture all nuances of individual affect. Individuals with depression diagnoses showed a higher degree of negative affect variability compared to individuals with anxiety disorders. Our findings support the idea that negative affect variability can be measured using computational approaches on large-scale social media data and that social media data can be used to study naturally occurring mental health effects at scale.

CNN-optimized text recognition with binary embeddings for Arabic expiry date recognition

Recognizing Arabic dot-matrix digits is a challenging problem due to the unique characteristics of dot-matrix fonts, such as irregular dot spacing and varying dot sizes. This paper presents an approach for recognizing Arabic digits printed in dot matrix format. The proposed model is based on convolutional neural networks (CNN) that take the dot matrix as input and generate embeddings that are rounded to generate binary representations of the digits. The binary embeddings are then used to perform Optical Character Recognition (OCR) on the date images. To overcome the challenge of the limited availability of dotted Arabic expiration date images, we developed a True Type Font (TTF) for generating synthetic images of Arabic dot-matrix characters. The model was trained on a synthetic dataset of 3287 images and 658 synthetic images for testing, representing realistic expiration dates from 2019 to 2027 in the format of yyyy/mm/dd and yy/mm/dd. Our model achieved an accuracy of 98.94% on the expiry date recognition with Arabic dot matrix format using fewer parameters and less computational resources than traditional CNN-based models. By investigating and presenting our findings comprehensively, we aim to contribute substantially to the field of OCR and pave the way for advancements in Arabic dot-matrix character recognition. Our proposed approach is not limited to Arabic dot matrix digit recognition but can be also extended to text recognition tasks, such as text classification and sentiment analysis.

A Comprehensive Review of Text Sentiment Analysis: A Survey of Traditional Methods and Deep Learning Approaches

Text sentiment analysis is a crucial aspect within the realm of natural language processing. This paper, incorporating the latest research advancements, systematically reviews and summarizes mainstream methods in text sentiment analysis. It covers a spectrum of traditional text analysis techniques, including rule-based and dictionary-based methods, as well as machine learning approaches. The paper further delves into an in-depth discussion of text sentiment analysis methods based on deep learning. This encompasses Recurrent Neural Networks and their enhanced structures, Convolutional Neural Networks and their extended techniques, along with applications involving pre-trained models and transfer learning.

Sentiment Analysis by Double Classification of Takeaway Platform Reviews Based on Deep Learning LSTM Models

Sentiment analysis has a wide range of applications in the fields of opinion analysis, sentiment dialog, and product reviews. However, the sentiment information expressed in texts under different topics varies greatly; for example, a model that performs well on a movie review set has poor model classification on a social platform review set due to inconsistent recognition of antiphonal phrases, different expression of emoji sentiment, and missing contextual information. In this paper, the authors focus on tens of thousands of latest reviews of Chinese takeout platforms Meituan and Elema, and use the LSTM model in deep learning to double classify the data (positive and negative). This paper analyzes the performance of LSTM models in the field of sentiment analysis of takeout reviews and concludes that domain-specific text sentiment analysis requires specific analysis.

Exploring online consumer experiences and experiential emotions offered by travel websites that accept cryptocurrency payments

Digital marketers are becoming increasingly aware of people’s online consumer experiences owing to the increasing popularity of online consumption. Exceptional online consumer experiences and memorable encounters are crucial. Although the use of cryptocurrency payments has become a new area for online website business growth, research on the experiential attributes of online travel websites supporting cryptocurrency payments is still lacking. The current research utilized text analytics and sentiment analysis to analyze reviews from seven online travel websites that accept cryptocurrency payments. This study identified 17 online experience topics and examine their relationship with ratings. Thereafter, perform emotional analysis in the comments. This research aimed to clarify the experiential aspects and emotional responses associated with online travel websites that accept cryptocurrency payments, ultimately contributing to a profound understanding of consumer experiences in this domain. Result provides new insights into the online experiences of online travel websites that accept cryptocurrency payments and contributes to theoretical and practical implications in this field.

Comparison of Machine Learning Approaches for Sentiment Analysis in Slovak

The process of determining and understanding the emotional tone expressed in a text, with a focus on textual data, is referred to as sentiment analysis. This analysis facilitates the identification of whether the overall sentiment is positive, negative, or neutral. Sentiment analysis on social networks seeks valuable insight into public opinions, trends, and user sentiments. The main motivation is to enable informed decisions and an understanding of the dynamics of online discourse by businesses and researchers. Additionally, sentiment analysis plays a vital role in the field of hate speech detection, aiding in the identification and mitigation of harmful content on social networks. In this paper, studies on the sentiment analysis of texts in the Slovak language, as well as in other languages, are introduced. The primary aim of the paper, aside from releasing the “SentiSK” dataset to the public, is to evaluate our dataset by comparing its results with those of other existing datasets in the Slovak language. The “SentiSK” dataset, consisting of 34,006 comments, was created, specified, and annotated for the task of sentiment analysis. The proposed approach involved the utilization of three datasets in the Slovak language, with nine classification methods trained and compared in two defined tasks. For the first task, testing on the “SentiSK” and “Sentigrade” datasets involved three classes (positive, neutral, and negative). In the second task, testing on the “SentiSK”, “Sentigrade”, and “Slovak dataset for SA” datasets involved two classes (positive and negative). Selected models achieved an F1 score ranging from 75.35% to 95.04%.

Autoregressive Feature Extraction with Topic Modeling for Aspect-based Sentiment Analysis of Arabic as a Low-resource Language

This paper proposes an approach for aspect-based sentiment analysis of Arabic social data, especially the considerable text corpus generated through communications on X (formerly known as Twitter) for expressing opinions in Arabic-language tweets during the COVID-19 pandemic. The proposed approach examines the performance of several pre-trained predictive and autoregressive language models; namely, Bidirectional Encoder Representations from Transformers (BERT) and XLNet, along with topic modeling algorithms; namely, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), for aspect-based sentiment analysis of online Arabic text. In addition, Bidirectional Long Short Term Memory (Bi-LSTM) deep learning model is used to classify the extracted aspects from online reviews. Obtained experimental results indicate that the combined XLNet-NMF model outperforms other implemented state-of-the-art methods through improving the feature extraction of unstructured social media text with achieving values of 0.946 and 0.938, for average sentiment classification accuracy and F-measure, respectively.

Text emotional analysis in Natural Language Processing

Natural Language Processing (NLP) is an important research direction in the field of artificial intelligence, aimed at enabling computers to understand and process human natural language. Emotional analysis refers to the use of computer technology to automatically recognize and classify the emotional tendencies expressed in text, such as positive, negative, or neutral. This technology can be applied to fields such as social media analysis, public opinion monitoring, market research, etc. The goal is to enable computers to understand and interpret emotional information in text like humans. Text sentiment analysis, as a key task of NLP, aims to identify and classify emotional tendencies expressed in text. This article will delve into the field of text sentiment analysis in natural language processing, focusing on the role and application of standard sentiment dictionaries and corpora in sentiment analysis. Reviewing relevant research conclusions, providing reference and inspiration for achieving more accurate and comprehensive text sentiment analysis through research and exploration.

Sarcasm detection methods based on machine learning and deep learning

Sarcasm is an expression of emotion, which often expresses criticism and disgust in a positive tone, and we see it everywhere on social networking platforms. Because of this nature, sarcasm can be challenging to process in Natural Language Processing (NLP) Systems. Failing to detect sarcasm correctly can negatively affect the results of natural language processing tasks such as text correct and sentiment analysis. In recent years, there are more and more studies on sarcasm detection, and the research methods are diverse. According to the survey, the methods based on machine learning (ML) and deep learning (DL) account for the majority. Therefore, this paper mainly summarizes the literature on sarcasm detection based on ML and DL methods in recent years. This paper first introduces the application of sarcasm, then expounds the general architecture of sarcasm detection and makes a distinction between machine learning and deep learning. Then the author investigates the relevant literature and compares the results. Finally, the challenges and research directions in this field are summarized. The purpose of this paper is to facilitate the follow-up research of sarcasm detection.

Sentiment analysis of hotel comments based on LSTM and GRU

Sentiment analysis, which tries to examine the emotional information in the provided text data, has always been a popular topic in the community of natural language processing. Sentiment analysis is currently used in many different contexts, including e-commerce platforms, social media platforms, public opinion platforms, and chatbots. These applications are crucial to the advancement of society and the domestic economy. However, due to the personalization of text data, especially comments, and the presence of acronyms, it is a challenging problem to obtain accurate sentiment information from large and complex unstructured text data. This study presents a comparative examination of various text sentiment analysis approaches, including LSTM, CNN, and GRU. These methods are employed to evaluate their respective performance on sentiment analysis tasks, specifically using a dataset of hotel reviews for training the models. The method presented in this research has been extensively validated through numerous experimental results, affirming its efficacy and its potential to offer novel perspectives for the practical implementation of sentiment analysis.