CVAE-Attention

The research on text sentiment classification has been very popular. The main methods include the methods based on rules and lexicons, machine statistical learning, and neural network methods. These methods have achieved great success in text sentiment classification, but there are still many defects. For traditional methods, its versatility is poor, once the text style or format changes significantly, the rules need to be re-established. Although the neural network method is generally applicable, it is not sufficient for the application of language knowledge. In order to solve the above problems, we propose a new model combining the traditional method and neural network method. It combines transition lexicon, stacked BILSTM and attention mechanism, and uses word2vec to initialize word vectors. For sentiment classification, semantic features are necessary, stacked BILSTM can capture contextual semantics multiple times to obtain richer contextual information representation. At the same time, we consider that the polarity of the clauses before and after the transition words may be completely opposite, which will also have a significant impact on the classification results. Therefore, we propose two strategies for transition words. One is to use the transition lexicon to supervise the attention mechanism and make the attention model sensitive to the transition words. The other is to cut the sentence according to the transition words to eliminate the noise of irrelevant items in the sentence. Our method combines the advantages of traditional methods and neural network methods, and makes up for their respective shortcomings. We use the proposed method to conduct a sentimental binary classification experiment on three data sets. The experimental results show that our method is superior than the previous methods.

ABSTRACTWith diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.

Nowadays, an abundance of short text is being generated that uses nonstandard writing styles influenced by regional languages. Such informal and code-switched content are under-resourced in terms of labeled datasets and language models even for popular tasks like sentiment classification. In this work, we (1) present a labeled dataset called MultiSenti for sentiment classification of code-switched informal short text, (2) explore the feasibility of adapting resources from a resource-rich language for an informal one, and (3) propose a deep learning-based model for sentiment classification of code-switched informal short text. We aim to achieve this without any lexical normalization, language translation, or code-switching indication. The performance of the proposed models is compared with three existing multilingual sentiment classification models. The results show that the proposed model performs better in general and adapting character-based embeddings yield equivalent performance while being computationally more efficient than training word-based domain-specific embeddings.

With the development of modern information technology, social networks have become an im-portant platform for people to express, and at the same time, a large number of texts have been produced. However, the comment text has the characteristics of randomness and colloquialism in the way of expression, and also contains a lot of non-text data. Therefore, manually analyzing the emotional information in the text will consume a lot of time and the accuracy will be limited. To solve emotion classification, this study proposes a knowledge enhanced double loop emotion classification neural network model with attention mechanism. The study first preprocesses text data using a full sentence vector word vector model, then uses convolutional neural networks to recognize emotions in emoticons and emoticons in the text. Finally, the classification results are integrated using algorithms such as a dual loop sentiment classification neural network with pool-ing layers and attention mechanisms, K-nearest neighbors, and decision trees. The final compre-hensive expression recognition and text recognition results are used to obtain the text sentiment classification results. The experimental data shows that the model proposed in this study has an accuracy of 0.947 in the training set test, which is significantly better than other models. In da-tasets A and B, the accuracy of the research design model was 0.958 and 0.924, and the recall was 0.964 and 0.986, respectively. Compared to the baseline method or existing research models, the values of each indicator were significantly higher. The recall rate is the proportion of instances correctly identified as positive by the model to all actual positive instances, which can reflect the emotional classification performance of the research and design model. The higher the value, the better the performance of the model. In practical applications, the positive review rate of this model is above 0.9, which has obvious advantages compared to other models. This study utilizes deep learning techniques to classify sentiment in comment texts, providing reference for the field of text sentiment classification. In the e-commerce industry, it is possible to identify the emotions in user comments on products, further understand the product situation on the platform, and make targeted planning for product reserves, specifications, and so on.

Aiming at the problem that traditional convolutional neural networks cannot fully capture text features during feature extraction, and a single model cannot effectively extract deep text features, this paper proposes a text sentiment classification method based on the attention mechanism of LSTM-TCN hybrid model. First, use the Word2vec word vector model to transform words into the form of word vectors. Secondly, with Long Short-Term Memory (LSTM) to obtain the serialized feature information of the text, and then combined with the Temporal Convolutional Network (TCN) to sample and calculate the text features to extract more comprehensive feature information of the text. Then, combined with the attention mechanism, the influence of important information on the sentiment of the text is considered, and the effect of text sentiment classification is optimized. Finally, use softmax function to classify sentiment in the text classification layer. Multiple sets of comparative experiments were conducted on two sets of Chinese data sets, and the accuracy of the text model reached 93.06% and 91.08%, respectively, proving that the proposed model performs better than the traditional single convolutional neural network.

With the rapid expansion of user-generated content on social media platforms like Twitter, Facebook, and Reddit, accurately identifying sentiment from textual data has become an essential yet challenging task due to the informal, noisy, and contextually diverse nature of these platforms. To address this, we propose a Hybrid Attention-Driven Recurrent Neural Network (HA-RNN) model that effectively combines Bidirectional Gated Recurrent Units (Bi-GRU) with a sophisticated attention mechanism for sentiment classification. The model utilizes pre-trained GloVe embeddings (300 dimensions) to capture rich semantic features from raw text, enhancing the initial representation of social media data. The Bi-GRU layers are employed to model sequential dependencies Safeyah Tawil Department of Computer Science and Engineering, Faculty of Information Technology, Zarqa University, Zarqa, Jordan. University of Business and Technology, Jeddah, Saudi Arabia stawil@zu.edu.jo I. INTRODUCTION Social media platforms have become primary channels for individuals to express opinions, emotions, and sentiments on a wide range of topics, including politics, products, services, and global events. The explosive growth of platforms such as Twitter, Facebook, and Instagram has led to an overwhelming amount of unstructured textual data that offers valuable insights into public sentiment [1]. Analyzing this vast content can support businesses, governments, and researchers in understanding user perceptions, improving services, and detecting social trends. in both forward and backward directions, ensuring a comprehensive understanding of context within a sentence. The integrated attention layer enables the model to dynamically focus on sentiment-bearing words, thereby improving classification accuracy and interpretability. We evaluated the proposed model on two widely recognized datasets: the Twitter US Airline Sentiment Dataset and the Sentiment140 Dataset. The HA-RNN achieved an accuracy of 90.8% on the Twitter US Airline dataset and 88.5% on Sentiment140, outperforming traditional models such as CNN (84.3% accuracy), LSTM (86.7%), and Bi-GRU without attention (87.1%). Furthermore, the attention mechanism provided insightful visualization, highlighting the critical words influencing sentiment predictions. The model demonstrated a balanced performance with high precision, recall, and F1-scores, validating its robustness across different sentiment classes. Overall, the HA- RNN model presents an effective and interpretable solution for sentiment analysis on noisy and diverse social media texts, supporting applications in social monitoring, brand analysis, and opinion mining.

With the rapid increase of the number of Internet users and the amount of online comment data, a large number of referable information samples are provided for data mining technology. As a technical application of data mining, text sentiment classification can be widely used in public opinion management, marketing, and other fields. In this study, a combination approach to SVM (support vector machine) and IPSO (improved particle swarm optimization) is proposed to classify sentiment by using text data. First, the text data of 30,000 goods reviews and corresponding ratings are collected through the web crawler. Then, TFIDF (term frequency-inverse document frequency) and Word2vec are used to vectorize the goods review text data. Next, the proposed classification model is trained by the SVM, and the initial parameters of the SVM are optimized by the IPSO. Finally, we applied the trained SVM-IPSO model to the test set and evaluated the performance by several measures. Our experiment results indicate that the proposed model performed the best for text data sentiment classification. Additionally, the traditional machine learning model SVM becomes very effective after parameter optimization, which demonstrates that the parameters’ optimization by IPSO has successfully improved the classification accuracy. Furthermore, our proposed model SVM-IPSO significantly outperforms other benchmark models, indicating that it could be applied to improve the accuracy and efficiency for text data sentiment classification.

Supervised learning methods are widely used in text sentiment classification. To acquire high classification performance, the effective and precise term weighting scheme plays a prime and necessary role for classification system. The traditional term weighting schemes often ignore the use of the available labeling information as the prior knowledge, which results the expressed relationships between features and class labels are not accurate and adequate enough. Hence, this paper proposes a term weighting scheme which makes use of class contribution of feature terms, and obtains a scheme which takes into account the class contribution, local distribution and global distribution of feature terms. The scheme expresses the relationships between features and class labels as a class contribution degree factor which is based on posterior probability, then combines it with the factors of frequency and inverse document frequency. Compared with a series of term weighting schemes, experimental results on a widely used dataset show that the proposed scheme can significantly improve the performance of sentiment classification.

Sentiment analysis mainly studies the emotional tendencies of texts from grammar, semantic rules and other aspects. The texts from social network are characterized by less words, irregular grammar, data noise and so on, which have increased the difficulty of emotion analysis. In order to improve the performance of machine learning in sentiment analysis, this study proposed the Majority Decision Algorithm to classify the emotional tendentious of the text in WeChat, combined the characteristics of five classifiers and integrated the classification results of five classifiers, eventually the text can be classified in WeChat. Firstly, this study utilized the BlueStacks to crawl the cache of WeChat Moment developed by Tencent company. Secondly, the cache was processed by Python to get the WeChat dataset. After the Chinese word segmentation, data cleaning and segmentation, the sentiment classification experiment were carried out using different classifiers. Finally, a Majority Decision Algorithm composed of five classifiers was established. It included, Naive Bayes (sklearn), Naive Bayes (SnowNLP), SVM (linear), SVM (RBF) and SGD. Then, the comparison was carried out between the performance of the algorithm and the five classifiers. Results show that the precision rates of the five classifiers are 0.8598, 0.8154, 0.8511, 0.8739 and 0.8678; the recall rates are 0.8544, 0.8482, 0.9380, 0.9226 and 0.9349; F1 scores are 0.8571, 0.8315, 0.8924, 0.8975 and 0.9001, respectively. The algorithm of the Precision rate, Recall rate and F1 score were 0.8804, 0.9349 and 0.9069, respectively, indicating that algorithm in current study significantly improved the performance, which can be effectively applied into the new text form of WeChat Moment. The study can provide theoretical reference for sentiment classification of Chinese text based on machine learning.

This paper considers the task of sentiment classification of subjective text across many domains, in particular on scenarios where no in-domain data is available. Motivated by the more general applicability of such methods, we propose an extensible approach to sentiment classification that leverages sentiment lexicons and out-of-domain data to build a case-based system where solutions to past cases are reused to predict the sentiment of new documents from an unknown domain. In our approach the case representation uses a set of features based on document statistics, while the case solution stores sentiment lexicons employed on past predictions allowing for later retrieval and reuse on similar documents. The case-based nature of our approach also allows for future improvements since new lexicons and classification methods can be added to the case base as they become available. On a cross domain experiment our method has shown robust results when compared to a baseline single-lexicon classifier where the lexicon has to be pre-selected for the domain in question.

Under the background of the gradual development and popularization of mobile Internet information technology, this paper realizes network public opinion monitoring and emotion analysis based on the deep learning method, aiming at the research needs of people's ideological changes and emotional trends. Aiming at the shortcomings of sentiment dictionaries or machine learning methods in sentiment analysis tasks, this paper builds a sentiment classification model based on deep learning methods. First, the current main text preprocessing methods are introduced, and then a sentiment classification model, BCBL, is proposed, combining BERT, CNN, and Bi LSTM. Compared with traditional models, BCBL can better complete text sentiment classification tasks on standard datasets. Next, in view of the problem that BCBL does not consider the distribution of vocabulary weights, an attention mechanism is introduced to improve BCBL, and then the BCBL-Att model is proposed. Set up multiple sets of comparative experiments again and find that the classification effect and overall performance of BCBL-Att on standard datasets are better than BCBL, indicating that BCBL-Att has more advantages in text sentiment classification tasks.

The rapid growth of financial technology (fintech) services in Indonesia has significantly transformed digital transaction behavior, with digital wallets such as DANA becoming widely adopted. Despite high usage rates, public discourse on social media frequently highlights both positive experiences and recurring concerns related to system reliability and data security. Twitter, as a real-time public communication platform, provides a rich source of user-generated content that reflects public perception toward fintech services. This study investigates public sentiment toward DANA fintech services in Indonesia through sentiment analysis of Twitter data using machine learning approaches. Purpose:This study aims to analyze public perception of DANA fintech services based on sentiment expressed on Twitter and to compare the performance of several machine learning classification algorithms in identifying positive, neutral, and negative sentiments in Indonesian-language social media texts. Methods/Study design/approach: The study employs a quantitative descriptive approach using text mining and sentiment analysis techniques. Twitter data were collected via the Twitter API over a specified period and processed through preprocessing stages including text cleaning, tokenization, stopword removal, and stemming. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Sentiment classification was conducted using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest algorithms. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Result/Findings:The results indicate that public sentiment toward DANA is predominantly positive, driven by perceptions of transaction convenience and speed, followed by neutral sentiment, while negative sentiment is mainly associated with system disruptions and data security issues. Among the evaluated algorithms, SVM achieved the highest classification accuracy (91.4%), outperforming Random Forest and KNN. These findings demonstrate that SVM is more effective for sentiment classification of short Indonesian-language texts on social media platforms. Novelty/Originality/Value:This study contributes to the growing body of sentiment analysis research in Indonesia by providing empirical evidence on public perception of fintech services using machine learning techniques. By systematically comparing multiple classification algorithms in the context of Indonesian Twitter data, the study offers methodological insights and practical implications for fintech service providers in improving service quality, communication strategies, and user trust.

The explosive growth of social media platforms has generated an unprecedented volume of user-generated content, making sentiment classification a crucial task for understanding public opinion, brand perception, and societal trends. However, the informal language, context-dependent expressions, and sequential dependencies in social media texts pose significant challenges for conventional machine learning and deep learning models. To address these challenges, this study proposes a Hybrid Attention-Driven Recurrent Neural Network (HADRNN) model for sentiment classification of social media texts. The model integrates bidirectional gated recurrent units (Bi-GRUs) with a multi-level attention mechanism, enabling it to effectively capture both global semantic context and fine-grained sentiment cues. Pre-trained GloVe embeddings are employed for robust lexical representation, while convolutional layers extract local n-gram features that complement the sequential modeling capabilities of recurrent units. The attention module adaptively emphasizes sentiment-bearing words and contextual phrases, ensuring both interpretability and improved performance. Extensive experiments were conducted on benchmark datasets including Twitter Sentiment140 and IMDB reviews. The proposed HADRNN achieved an accuracy of 92.1%, F1-score of 91.3%, precision of 91.7%, and recall of 90.8% on Twitter Sentiment140, significantly outperforming baseline CNN (87.2% accuracy), LSTM (88.9% accuracy), and Transformer-based models (90.4% accuracy). On IMDB, the model obtained an accuracy of 94.6% and F1-score of 94.1%, demonstrating robustness to longer text sequences and noisy language. These findings confirm that the hybrid integration of recurrent dynamics, attention, and convolutional processing not only enhances classification performance but also provides interpretable insights into sentiment-laden expressions, offering a reliable framework for real-world social media analytics.

Traditional word vector generation model cannot solve the polysemy problem of word representation in Twitter text, so, a method is presented here, firstly, by using Bidirectional Encoder Representations from Transformers (BERT), the semantic feature vector of the text can be obtained, and then, the feature vector is inputted into the Softmax classifier to implement the sentiment classification of Twitter text. The experimental datasets are source from passengers’ Twitter comments of USA six airlines, the sentiment classification model based on Embeddings from Language Model (ELMo) as the experimental control group. The experimental results indicate that the proposed model is advantage over the experimental control group by using F1-score as the evaluation index.

Aiming at the shortage of research on micro-blog short text fine-grained sentiment classification, a fine-grained sentiment classification method about micro-blog short text based on PLSA model and K-means clustering model was proposed. PLSA is used to calculate the probability matrix between documents and topics, words and topics in the corpus. In terms of the probability distribution of words and topics, K-means algorithm is used to cluster the probability distribution of words on topics and merge the similar topics. Based on the sentiment ontology library, emotion recognition is carried out for the merged topics. Then, according to the merged document and topic probability matrix, the document sentiment category is classified. The experimental results show that the sentiment analysis method integrated with PLSA and K-means can obtain higher classification accuracy than the PLSA model method alone.