Text Sentiment Analysis Research Articles

The ninth art, tells the story of cultural China and the world: Cultural interaction in cross-cultural video game communication-based on 'Genshin Impact' YouTube comments.

China has attached increasing importance to the overseas dissemination of traditional culture as it continues to strengthen its comprehensive national strength and develop globalization initiatives. The electronic game, as the 'ninth art', with its booming development and inherent cross-cultural communication platform attributes, has taken on the important task of taking culture abroad. This study selects 'Genshin Impact', a game highly praised and well-received globally, which contains an abundance of Chinese cultural elements and cultural elements from various regions of the world, to study cultural interaction in cross-cultural communication of electronic games. This study conducts text sentiment analysis and semantic network analysis on the comments of 'Genshin Impact' videos on the overseas video platform YouTube and studies the cultural interaction in the cross-cultural communication of the 'ninth art', telling the cultural stories of China and the world from a multicultural perspective. The study found that games and derivative videos triggered positive emotional responses from audiences both at home and abroad. While experiencing pleasure and surprise, audiences also arouse deeper emotions such as admiration, identification, and expectation. Meanwhile, in the complex art form of the 'ninth art', audiences can absorb the cultural elements of different cultures, resonate with the elements of their cultural circle, and even trigger further profound thinking. Finally, this study provides constructive suggestions on using the 'ninth art' to better 'tell China's stories and disseminate China's voice'.

Predicting customer sentiment: the fusion of deep learning and a fuzzy system for sentiment analysis of Arabic text

Predicting customer sentiment: the fusion of deep learning and a fuzzy system for sentiment analysis of Arabic text

A text sentiment analysis method based on BiGRU and capsule network

Most of the existing sentiment analysis methods based on product review texts rarely consider the aspect features of the review texts, and the relevant analysis models do not consider the long-term contextual dependency features and local text features at the same time, thus affecting the accuracy of sentiment analysis. A text sentiment analysis method based on a bidirectional gated recurrent network (BiGRU) and a capsule network is proposed. This method first uses a word frequency statistics-based method to extract the aspect features of the review text and integrates them into the word vector representation, thereby effectively improving the expressive power of the word vector. Then, BiGRU is used to extract the long-term contextual dependency features of the text, and the capsule network is used to extract the local features of the text, thereby achieving high-precision text sentiment analysis based on aspects. Experimental results on real datasets show that the proposed method is superior to existing sentiment analysis models such as bidirectional long short-term memory network (BiLSTM), CNN-LSTM, and TextCNN in terms of evaluation indicators such as accuracy, precision, recall, and score.

A three-stage prediction model for firm default risk: An integration of text sentiment analysis

Predicting firm default risk is vital for financial institutions to avert significant economic losses, making the enhancement of its prediction precision both imperative and intricate. This research introduces a three-stage prediction model, including association rule algorithm (ARA), support vector machine (SVM) and modified particle swarm optimization algorithm (MPSO). Features selected by ARA are used as inputs for SVM, and penalty parameter and kernel parameter of SVM is optimized by MPSO that uses the adaptive inertia weight. The importance of text sentiment variables are emphasized to predict firm default risk. In the first stage, feature selection seeks to curtail the dimensions of both financial and non-financial variables. The empirical findings validate the efficacy of the ARA, revealing a strong correlation between text sentiment and default risk. The subsequent two stages deploy the SVM, refined by the MPSO, to predict the default risk. Compared with renowned models, the proposed model displays superior prediction precision and a reduced computational overhead. This research furnishes a potent instrument for regulators and firms alike, aiding in mitigating prospective default risks and forestalling broader economic upheavals.

Media Sentiment and Its Role in Shaping IPO Performance

The media performs an essential role in providing information regarding initial public offerings (IPOs) to investors. Often, public information about IPO companies is scarce and investors find it difficult to comprehend complex documents, making media a cost-effective and authentic source of information. This study explores how media sentiment affects the underpricing and initial aftermarket performance of IPOs using 3,124 media articles published from April 2019 to March 2022. In doing so, the authors employed textual sentiment analysis using the Loughran and McDonald dictionary to deduce the media net sentiment score. Subsequently, robust regression is conducted by incorporating the media sentiment, the volume of media articles, and other control variables. The result mentions that the way media present information has the potential to influence the decision of retail investors. The net sentiment score shows that the positive information in the media article makes investors optimistic about the prospects of the business. It boosts their confidence and creates more demand for shares, thereby increasing the IPO’s first-day market-adjusted abnormal return. Further, it has been discovered that media sentiment significantly influences initial aftermarket performance primarily due to the limited trading history available for the IPO company during the initial stage.

EMFSA: Emoji-based multifeature fusion sentiment analysis

Short texts on social platforms often suffer from insufficient emotional semantic expressions, sparse features, and polysemy. To enhance the accuracy achieved by sentiment analysis for short texts, this paper proposes an emoji-based multifeature fusion sentiment analysis model (EMFSA). The model mines the sentiments of emojis, topics, and text features. Initially, a pretraining method for feature extraction is employed to enhance the semantic expressions of emotions in text by extracting contextual semantic information from emojis. Following this, a sentiment- and emoji-masked language model is designed to prioritize the masking of emojis and words with implicit sentiments, focusing on learning the emotional semantics contained in text. Additionally, we proposed a multifeature fusion method based on a cross-attention mechanism by determining the importance of each word in a text from a topic perspective. Next, this method is integrated with the original semantic information of emojis and the enhanced text features, attaining improved sentiment representation accuracy for short texts. Comparative experiments conducted with the state-of-the-art baseline methods on three public datasets demonstrate that the proposed model achieves accuracy improvements of 2.3%, 10.9%, and 2.7%, respectively, validating its effectiveness.

User Sentiment Analysis Based on Securities Application Elements.

Designing securities applications for mobile devices is challenging due to their inherent complexity, necessitating improvement through the analysis of online reviews. However, research applying deep learning techniques to the sentiment analysis of Korean text remains limited. This study explores the use of Aspect-Based Sentiment Analysis (ABSA) as an effective alternative to traditional user research methods for securities application design. By analyzing large volumes of text-based user review data of Korean securities applications, the study identifies critical elements like "update", "screen", "chart", "login", "access", "authentication", "account", and "transaction", revealing nuanced user sentiments through techniques such as PMI, SVD, and Word2Vec. ABSA offers deeper insights compared to overall ratings, uncovering hidden areas of dissatisfaction despite positive biases in reviews. This research demonstrates the scalability and cost-effectiveness of ABSA in mobile-application design research.

A comprehensive review of visual–textual sentiment analysis from social media networks

A comprehensive review of visual–textual sentiment analysis from social media networks

Exploring public-private partnerships in Latin America and the Caribbean using topic modeling and sentiment analysis

Collaboration between the public and private sectors is crucial for supporting resource-constrained government budgets globally. Over the past two decades, studies have recorded significant advances in public-private partnerships (PPPs). However, PPPs in agribusiness within Latin America and the Caribbean (LAC) remain underexplored. This study analyzes the primary issues related to PPPs in agribusiness across selected countries and compares sentiments regarding these partnerships within the analyzed countries. Using text mining, topic modeling, and sentiment analysis, the study found that the main issues revolve around infrastructure, impacting trade, flow, and services in regional, national, and continental agribusiness and exports. While PPPs are seen as an innovative approach to improving public services, sentiment analysis reveals that many countries express anger and disgust, primarily due to concerns about corruption and the state’s inability to manage or play an effective role in these public policies. This study highlights that integrating topic and sentiment data offers a comprehensive view that can inform more effective public policies and development strategies for PPPs in agribusiness.

Performance Comparison of Support Vector Machines, AdaBoost, and Random Forest for Sentiment Text Analysis and Classification

In sentiment analysis, text analysis becomes an important process to derive useful information from the unstructured data. In this work, we study the performance of three advanced machine learning algorithms, Support Vector Machines (SVM), Random Forest, and AdaBoost, for a specific sentiment classification task. Each classifier was trained and evaluated on principal metrics such as Area Under the Curve (AUC), Classification Accuracy (CA), F1-Score, Precision, and Recall using the Bag of Words model for feature extraction. Those results show that the Random Forest approach beat both SVM and AdaBoost, with an AUC of 0.988, CA = 0.915, and F1-Score = 0.915 SVM demonstrated moderate performance with an AUC of 0.939 and an F1-Score of 0.845, while AdaBoost exhibited the worst performance in all metrics based on that ensemble model-based classifiers for data change predictions. Random Forest may thus be a powerful machine learning technique to implement for sentiment analysis in text.

A combined AraBERT and Voting Ensemble classifier model for Arabic sentiment analysis

For sentiment analysis of short texts (e.g. movie reviews, tweets, etc.), one approach is to build machine learning models that can determine their tones (positive, negative, neutral). However, these natural language processing (NLP) studies are missing when there is a lack of high-quality and large-scale training data for specific languages such as Arabic. In this paper, we present three machine learning models designed to classify sentiment Arabic tweets developed for a Kaggle competition. We present a Voting Ensemble classifier taking advantage of both character-level and word-level features. We also propose an AraBERT (Arabic Bidirectional Encoder Representations from Transformers) model with preprocessing using Farasa Segmenter. Finally, we combine these first two approaches as a third approach (Voting Ensemble classifier using AraBERT embeddings). Performance measures of results show improvement over previous efforts for all models. The third model exhibits strong performance with a 73.98% F-score score. The work presented here could be useful for future studies and for new Arabic sentiment analysis online services or competitions.

AIoT based sentiment analysis via multi‐scale context perception to enhance linguistic teaching

AbstractWith the rapid development of the Internet of Things (IoT) and artificial intelligence technology, real‐time text sentiment analysis plays an important role in online education. Due to the limited resources of clients, existing deep networks cannot be directly deployed in the edge node. In addition, deep convolutional networks cannot fully utilize contextual information. In order to resolve these issues, this paper proposes a multi‐scale context‐aware text sentiment analysis system based on cloud computing, in which a bidirectional long short‐term memory network (BiLSTM) model is deployed in the cloud server. The BiLSTM model can fully explore the contextual feature information of the text stream in online education. The real‐time text data are collected through terminal nodes, such as pad or computer, to send the cloud server. The experiments utilize three public text datasets to simulate the input of terminal nodes. The results show that the proposed system shows better accuracy than previous models and can return the emotional status in time.

Attention-based fuzzy neural networks designed for early warning of financial crises of listed companies

Developing an early warning model for company financial crises holds critical significance in robust risk management and ensuring the enduring stability of the capital market. Although the existing research has achieved rich results, the disadvantages of insufficient text information mining and poor model performance still exist. To alleviate the problem of insufficient text information mining, we collect related financial and annual report data from 820 listed companies in mainland China from 2018 to 2023 by using sophisticated web crawlers and advanced text sentiment analysis technologies and using missing value interpolation, standardization, and data balancing to build multi-source datasets of companies. Ranking the feature importance of multi-source data promotes understanding the formation of financial crises for companies. In the meantime, a novel Attention-based Fuzzy Neural Network (AFNN) was proposed to parse multi-source data to forecast financial crises among listed companies. Experimental results indicate that AFNN exhibits significantly improved performance compared to other advanced methods.

The Standardized Application of Data Mining Algorithm Based on Computational Linguistics in English Teaching

With the rapid development of computational linguistics and data mining technology, the field of education is gradually exploring its potential applications in teaching. This paper proposes a standardized application method based on data mining algorithms, aiming to improve the systematicity and effectiveness of English teaching. Firstly, we apply data mining techniques to analyze the characteristics of English computer language in order to construct a data-driven teaching foundation. Subsequently, through in-depth analysis of these data, we designed and implemented an evaluation of the mining effect in English teaching classrooms. To ensure consistency and effectiveness in English teaching, we have established a computational linguistics English teaching model based on data mining algorithms. This model covers multiple aspects such as text classification, sentiment analysis, and semantic understanding, providing more accurate and personalized teaching and learning experiences for English teachers and students. After testing the model, we found that it can meet the requirements of teaching design and usage, bringing significant impact to English teaching. This paper provides a detailed introduction to the standardized application process of data mining algorithms in English teaching, and explores their positive effects on improving the quality of English teaching.

Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis

Standard machine learning and deep learning architectures have been widely used in the field of sentiment analysis, but their performance is unsatisfactory if the input texts are short (e.g., social media posts). Specifically, the accuracy of standard machine learning methods crucially depends on the richness and completeness of the features used to represent the texts, and in the case of short messages, it is often difficult to obtain high-quality features. Conversely, methods based on deep learning can achieve better expressiveness, but these methods are computationally demanding and often suffer from over-fitting. This paper proposes a new adaptive evolutionary computational integrated learning model (AdaECELM) to overcome the problems encountered by traditional machine learning and deep learning models in sentiment analysis for short texts. AdaECELM consists of three phases: feature selection, sub classifier training, and global integration learning. First, a grid search is used for feature extraction and selection of term frequency-inverse document frequency (TF-IDF). Second, cuckoo search (CS) is introduced to optimize the combined hyperparameters in the sub-classifier support vector machine (SVM). Finally, the training set is divided into different feature subsets for sub-classifier training, and then the trained sub-classifiers are integrated and learned using the AdaBoost integrated soft voting method. Extensive experiments were conducted on six real polar sentiment analysis data sets. The results show that the AdaECELM model outperforms the traditional ML comparison methods according to evaluation metrics such as accuracy, precision, recall, and F1-score in all cases, and we report an improvement in accuracy exceeding 4.5%, the second-best competitor.

AlgoDM: algorithm to perform aspect-based sentiment analysis using IDistance matrix

Sentiment analysis is a method of analyzing data to identify its intent. It identifies the emotional tone of a text body. Aspect-based sentiment analysis is a text analysis technique that identifies the aspect and the sentiment associated with each aspect. Different organizations use aspect-based sentiment analysis to analyze opinions about a product, service, or idea. Traditional sentiment analysis methods analyze the complete text and assign a single sentiment label to it. They do not handle the tasks of aspect association, dealing with multiple aspects and inclusion of linguistic concepts together as a system. In this article, AlgoDM, an algorithm to perform aspect-based sentiment analysis is proposed. AlgoDM uses a novel concept of IDistance matrix to extract aspects, associate aspects with sentiment words, and determine the sentiment associated with each aspect. The IDistance matrix is constructed to calculate the distance between aspects and the words expressing the sentiment related to the aspect. It works at the sentence level and identifies the opinion expressed on each aspect appearing in the sentence. It also evaluates the overall sentiment expressed in the sentence. The proposed algorithm can perform sentiment analysis of any opinionated text.

English Sentiment Analysis and its Application in Translation Based on Decision Tree Algorithm

Sentimental analysis belongs to the class of Natural Language Processing (NLP) based on the rule and machine model. The proposed model comprises of the pre-defined function for the estimation of the features in the English statements. This paper presents the Reflect Sentiment Translation Decision Tree (RSTDT), a novel model designed to integrate sentiment analysis and translation tasks for English text. The RSTDT model combines the strengths of decision tree algorithms with feature extraction techniques to accurately analyze sentiment and translate text across languages. The proposed RSTDT dataset comprises English sentences with annotated sentiment labels, the RSTDT model is trained to identify sentiment polarity and generate corresponding translations in Arabic. The proposed RSTDT model uses Traslation mapping for the estimation of the sentimental features. In order to estimate and classify the features in the neural network, the processes features are assessed using the decision tree model. The RSTDT model's efficacy in precisely capturing sentiment nuances and generating linguistically appropriate translations was shown through thorough testing and review. The model achieves high accuracy in sentiment analysis and exhibits proficiency in translating sentiment-rich content into Arabic while maintaining contextual relevance. Additionally, robust classification performance metrics underscore the model's efficacy in accurately classifying English words into sentiment categories. The RSTDT model offers a promising solution for multilingual sentiment analysis applications, with potential applications in social media monitoring, customer feedback analysis, and cross-cultural sentiment analysis.

Virtual human on social media: Text mining and sentiment analysis

Virtual humans are embodied agents with a human-like appearance. Despite the recent booming development that has sparked widespread academic interest, how people perceive these seemingly human but entirely fictional creations remains unclear. To explore the status, trends, emotional tendencies, and focus of attention of the Chinese public towards virtual humans, this paper utilizes text mining techniques to collect and analyze popular posts related to virtual humans on Chinese social media. The results indicate that public discussions primarily focus on the technological and industrial development of virtual humans, applications in the fields of virtual idols and virtual streamers, and the corporate investment and policy development of virtual humans. Despite positive emotions dominating, there is an increasing trend in negative emotions. Concerns are related to service failures, the uncanny valley effect, ethical crises, and technological unemployment. The research findings contribute to policymakers, industry stakeholders, and the public in understanding the general attitudes toward virtual human technology, enabling informed decision-making.

Text sentiment analysis and classification based on bidirectional long and short term memory networks

In this study, we explored the method of text sentiment analysis and classification using bi-directional long and short-term memory networks, which brings new ideas to the field of sentiment classification. In the text pre-processing stage, we processed chat phrases, non-alphanumeric characters, deactivated words, numbers, special characters, blanks, and addresses to convert the raw text data into numerical data that can be processed by the model. Subsequently, word frequency statistics were conducted for texts of different mood types to analyse the frequency of occurrence of different words in texts of different mood types. By introducing a bi-directional long and short-term memory network model for training, we observed the accuracy and loss value performance on the training and validation sets. The accuracy of the training set increases from the initial 94.6% to 99.5%, and the accuracy of the validation set gradually grows to 99.4%; at the same time, the loss value of the training set decreases from the initial value of 0.2075 to 0.0194, and the loss value of the validation set gradually increases from the initial value of 0.0538 to 0.0372. This indicates that the bi-directional long short-term memory network achieves a significant and stable performance improvement. The confusion matrix results for the test set show that our model achieves more than 99% prediction accuracy on the test set. This implies that our proposed method can efficiently classify textual sentiment with high prediction accuracy. This research result helps to improve the effectiveness of the application of sentiment analysis techniques in the fields of social media opinion monitoring and product review analysis, and provides a useful reference for further deepening the research on text sentiment analysis. In conclusion, this study achieves the optimisation of the text sentiment classification task by means of a bidirectional long and short-term memory network model, and achieves satisfactory results. In the future, the model performance and generalisation ability can be further improved by combining more corpus and optimising the model structure, so as to be better applied in real-world scenarios.

Text sentiment analysis and classification based on bidirectional Gated Recurrent Units (GRUs) model

This paper explores the importance of text sentiment analysis and classification in the field of natural language processing, and proposes a new approach to sentiment analysis and classification based on the bidirectional gated recurrent units (GRUs) model. The study firstly analyses the word cloud model of the text with six sentiment labels, and then carries out data preprocessing, including the steps of removing special symbols, punctuation marks, numbers, stop words and non-alphabetic parts. Subsequently, the data set is divided into training set and test set, and through model training and testing, it is found that the accuracy of the validation set is increased from 85% to 93% with training, which is an increase of 8%; at the same time, the loss value of the validation set decreases from 0.7 to 0.1 and tends to be stable, and the model is gradually close to the actual value, which can effectively classify the text emotions. The confusion matrix shows that the accuracy of the model on the test set reaches 94.8%, the precision is 95.9%, the recall is 99.1%, and the F1 score is 97.4%, which proves that the model has good generalisation ability and classification effect. Overall, the study demonstrated an effective method for text sentiment analysis and classification with satisfactory results.