Text Sentiment Classification Research Articles

A novel two-stage wrapper feature selection approach based on greedy search for text sentiment classification

Sentiment analysis is a crucial step in obtaining subjective data from online text sources. Nevertheless, the substantial challenge of high dimensionality prevails within text classification. Addressing this, dimension reduction emerges as a valuable approach to enhance the efficacy of classification in the domain of machine learning. The discerning removal of redundant features not only expedites training processes but also bolsters the achievement of accurate classifications. It is worth noting that the effectiveness of distinct feature selection methodologies can be contingent upon the unique attributes inherent in diverse datasets. Within the purview of this investigation, a novel two-stage approach is introduced, characterized by a greedy search-based wrapper feature selection algorithm. The underpinning of this algorithm involves leveraging the outcomes yielded by filter-based feature selection techniques to establish a prioritized sequence for the scrutiny of features within the proposed framework. This strategic sequencing harnesses the cumulative insights from a series of filter-based methodologies, thereby facilitating the curation of feature subsets that underscore pivotal attributes. Nonetheless, it is acknowledged that the greedy selection approach primarily favors features with high-ranking scores, and thus, it may not adequately evaluate the potential of feature combinations that involve low-scoring elements. An extensive experimental inquiry was conducted across widely recognized sentiment analysis datasets to assess the performance of the introduced methodology. The computational findings eloquently demonstrate that the proposed algorithm attains an average accuracy of 96.88% for nine public sentiment datasets and 94.43% for the Humir datasets when coupled with the multinomial Naive Bayes classifier. Furthermore, the experimental outcomes conspicuously establish the superiority of the proposed technique in state-of-the-art studies across the same set of nine sentiment datasets and the Humir datasets.

Text Emotion Classification System Integrating Visual Communication and Deep Learning for Social Platform

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.

Sentiment analysis of video danmakus based on MIBE-RoBERTa-FF-BiLSTM

Danmakus are user-generated comments that overlay on videos, enabling real-time interactions between viewers and video content. The emotional orientation of danmakus can reflect the attitudes and opinions of viewers on video segments, which can help video platforms optimize video content recommendation and evaluate users’ abnormal emotion levels. Aiming at the problems of low transferability of traditional sentiment analysis methods in the danmaku domain, low accuracy of danmaku text segmentation, poor consistency of sentiment annotation, and insufficient semantic feature extraction, this paper proposes a video danmaku sentiment analysis method based on MIBE-RoBERTa-FF-BiLSTM. This paper constructs a “Bilibili Must-Watch List and Top Video Danmaku Sentiment Dataset” by ourselves, covering 10,000 positive and negative sentiment danmaku texts of 18 themes. A new word recognition algorithm based on mutual information (MI) and branch entropy (BE) is used to discover 2610 irregular network popular new words from trigrams to heptagrams in the dataset, forming a domain lexicon. The Maslow’s hierarchy of needs theory is applied to guide the consistent sentiment annotation. The domain lexicon is integrated into the feature fusion layer of the RoBERTa-FF-BiLSTM model to fully learn the semantic features of word information, character information, and context information of danmaku texts and perform sentiment classification. Comparative experiments on the dataset show that the model proposed in this paper has the best comprehensive performance among the mainstream models for video danmaku text sentiment classification, with an F1 value of 94.06%, and its accuracy and robustness are also better than other models. The limitations of this paper are that the construction of the domain lexicon still requires manual participation and review, the semantic information of danmaku video content and the positive case preference are ignored.

Improving Turkish Text Sentiment Classification Through Task-Specific and Universal Transformations: An Ensemble Data Augmentation Approach

Improving Turkish Text Sentiment Classification Through Task-Specific and Universal Transformations: An Ensemble Data Augmentation Approach

Komparasi Naïve Bayes dan SVM Analisis Sentimen RUU Kesehatan di Twitter

This research focuses on sentiment analysis regarding the plan to ratify the Health Bill which has become a hot topic of conversation on social media, especially Twitter. This research aims to classify tweets that reflect various opinions regarding the Health Bill, including support, rejection and neutrality. In this research, the author uses two types of classification algorithms, namely the Multinomial Naïve Bayes Algorithm and the Support Vector Machine (SVM) Algorithm. Previously, tweets were labelled using the Lexicon InSet dictionary. The research was conducted in the Python programming language and using Google Collaboratory. Data validation was carried out using the K-fold cross-validation method. The research results indicate that both algorithms predominantly produce positive sentiments over negative ones. However, SVM with a linear kernel achieves a higher accuracy rate of 0.87, compared to Multinomial Naïve Bayes, which has an accuracy of 0.82. SVM also records a precision of 0.87, recall of 0.97, and an F1-score of 0.91, while Multinomial Naïve Bayes shows a precision of 0.81, recall of 0.98, and an F1-score of 0.89. Overall, this research confirms that SVM excels in text sentiment classification, while Multinomial Naïve Bayes also provides good results in recognising positive and negative sentiment. These results have important implications for understanding public sentiment regarding the Health Bill on the Twitter platform.

An Efficient Sentiment Classification Method with the Help of Neighbors and a Hybrid of RNN Models

Some texts that are challenging to recognize on their own may become more understandable in a neighborhood of related texts with similar contexts. Motivated by this intuition, a novel deep text sentiment classification (DTSC) model is proposed to improve the model’s performance by incorporating the neighborhood of related texts. Our framework uses a nonparametric approach to construct neighborhoods of related texts based on Jaccard similarities. Then, a new deep recurrent neural network architecture is proposed, comprising two distinct modules: bidirectional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU). The proposed model aims to effectively capture informative features from the input text and its neighbors. The result of each module is processed through the maximum operation, which selects the most pertinent data. Finally, the extracted features are concatenated and subjected to classification to achieve accurate sentiment prediction. Previous studies have commonly employed a parametric approach to represent textual metadata. However, our approach utilizes a nonparametric approach, enabling our model to perform strongly even when the text vocabulary varies between training and testing. The proposed DTSC model has been evaluated on five real-world sentiment datasets, achieving 99.60% accuracy on the Binary_Getty (BG) dataset, 98.32% accuracy on the Binary_iStock (BIS) dataset, 96.13% accuracy on Twitter, 82.19% accuracy on the multi-view sentiment analysis (MVSA) dataset, and 87.60% accuracy on the IMDB dataset. These findings demonstrate that the proposed model outperforms established baseline techniques in terms of model evaluation criteria for text sentiment classification.

Text Sentiment Classification Method Based on Bilstm

In the booming age of digital communication and natural language processing(NLP), understanding user sentiments expressed in texts has become increasingly vital. This research addresses the challenge of classifying text sentiments into three distinct categories: positive, neutral, and negative. Recognizing the nuances of human emotion in textual content is paramount for enhancing user experiences and tailoring personalized digital interactions. This study trains a BiLSTM model, supplemented with optimization strategies to adaptively learn from a diverse dataset. Additionally, a large number of baseline experiments were conducted to compare its efficacy with traditional algorithms such as Logistic Regression. The BiLSTM model demonstrated a good accuracy of 78.21% on the test set. Conclusively, while the proposed model shows significant potential, further refinements could be made, emphasizing data augmentation and model regularization to achieve optimal performance.

Text sentiment analysis based on BERT-CBLBGA

Current word vector techniques can generate effective word representations from massive text data, and different deep learning models can extract various textual semantic and grammatical features from different perspectives and levels. To extract richer text sentiment features, we propose a fused model of text sentiment classification called BERT-CBLBGA, which combines BERT with multiple feature extraction deep learning models. First, BERT is utilized to generate word embedding of the text, and the obtained feature representations are then fed into TextCNN, BiLSTM, and BiGRU to extract local and global sentiment features of the text. Then we use self-attention to fuse the output of TextCNN, BiLSTM, and BiGRU to enhance the ability to extract text sentiment classification features. Finally, a classifier is employed to classify the extracted features. Experimental results on four different domain text datasets demonstrate that the BERT-CBLBGA outperforms the baseline models in terms of extracting text sentiment features and achieves the best classification performance.

Social Image–Text Sentiment Classification With Cross-Modal Consistency and Knowledge Distillation

Social media sentiment analysis, which aims to evaluate the attitudes of online users based on their posts, has attracted significant research attention due to its successful application in the field of social media monitoring. It is a beneficial way to utilize multimodal information uploaded by users in order to improve sentiment classification ability. However, existing multimodal fusion-based approaches continue to face difficulties due to the issues of between-modality semantic inconsistency and missing modality. To address these issues, we propose a cross-modal consistency modeling-based knowledge distillation framework for image-text sentiment classification of social media data. Specifically, we design a hybrid curriculum learning strategy to measure the semantic consistency of multimodal data, then gradually train all image-text pairs from easy to hard, which can effectively handle the massive amounts of noise caused by inconsistencies between image and text data on social media. Moreover, in order to alleviate the problem of missing images in unimodal posts, we propose a privileged feature distillation method, in which the teacher model additionally considers images as privileged features, to transfer the visual knowledge to the student model, thereby enhancing the accuracy for text sentiment classification. Extensive experiments conducted over three real-world social media datasets demonstrate the effectiveness and superiority of the proposed multimodal sentiment analysis model.

Data augmentation for sentiment classification with semantic preservation and diversity

Data augmentation is a commonly-used technique to avoid over-fitting in deep learning. However, the mechanism behind effective data augmentation methods is unclear. To address this issue, we explore and identify two critical factors: semantic preservation and diversity to assess the quality of data augmentation in natural language processing. Our study focus on text sentiment classification and examines these two factors on two commonly-used data augmentation methods: synonym replacement and random deletion. Based on the discovery, we propose two new augmentation methods: TF-IDF word dropout and adaptive synonym replacement. Experimental results demonstrate that these two new data augmentation methods are effective. Moreover, with further experiments, we summarize three strategies for improving data augmentation methods in sentiment classification task. These strategies are employing online augmentation, introducing word importance into word sampling process, and filtering augmented data based on the current model state. We hope that our study will inspire some new perspectives on the underlying principles of data augmentation’s effectiveness and contribute to a systematic study of data augmentation methods in future.

Sentiment Analysis using a CNN-BiLSTM Deep Model Based on Attention Classification

With the rapid development of the Internet, the number of social media and e-commerce platforms increased dramatically. Users from all over world share their comments and sentiments on the Internet become a new tradition. Applying natural language processing technology to analyze the text on the Internet for mining the emotional tendencies has become the main way in the social public opinion monitoring and the after-sale feedback of manufactory. Thus, the study on text sentiment analysis has shown important social significance and commercial value. Sentiment analysis is a hot research topic in the field of natural language processing and data mining in recent ten years. The paper starts with the topic of "Sentiment Analysis using a CNN-BiLSTM deep model based on attention mechanism classification". First, it conducts an in-depth investigation on the current research status and commonly used algorithms at home and abroad, and briefly introduces and analyzes the current mainstream sentiment analysis methods. As a direction of machine learning, deep learning has become a hot research topic in emotion classification in the field of natural language processing. This paper uses deep learning models to study the sentiment classification problem of short and long text sentiment classification tasks. The main research contents are as follows. Firstly, Traditional neural network based short text classification algorithms for sentiment classification is easy to find the errors. The feature dimension is too high, and the feature information of the pool layer is lost, which leads to the loss of the details of the emotion vocabulary. To solve this problem, the Word Vector Model (Word2vec), Bidirectional Long-term and Short-term Memory networks (BiLSTM) and convolutional neural network (CNN) are combined in Quora dataset. The experiment shows that the accuracy of CNN-BiLSTM model associated with Word2vec word embedding achieved 91.48%. This proves that the hybrid network model performs better than the single structure neural network in short text. Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long- term dependencies between words hence are better used for text classification. However, even with the hybrid approach that leverages the powers of these two deep-learning models, the number of features to remember for classification remains huge, hence hindering the training process. Secondly, we propose an attention based CNN-BiLSTM hybrid model that capitalize on the advantages of LSTM and CNN with an additional attention mechanism in IMDB movie reviews dataset. In the experiment, under the control of single variable of Data volume and Epoch, the proposed hybrid model was compared with the results of various indicators including recall, precision, F1 score and accuracy of CNN, LSTM and CNN-LSTM in long text. When the data size was 13 k, the proposed model had the highest accuracy at 0.908, and the F1 score also showed the highest performance at 0.883. When the epoch value for obtaining the optimal accuracy of each model was 10 for CNN, 14 for LSTM, 5 for MLP and 15 epochs for CNN-LSTM, which took the longest learning time. The F1 score also showed the best performance of the proposed model at 0.906, and accuracy of the proposed model was the highest at 0.929. Finally, the experimental results show that the bidirectional long- and short-term memory convolutional neural network (BiLSTM-CNN) model based on attention mechanism can effectively improve the performance of sentiment classification of data sets when processing long-text sentiment classification tasks. Keywords: sentiment analysis, CNN, BiLSTM, attention mechanism, text classification

An Effective Personality-Based Model for Short Text Sentiment Classification Using BiLSTM and Self-Attention

While user-generated textual content on social platforms such as Weibo provides valuable insights into public opinion and social trends, the influence of personality on sentiment expression has been largely overlooked in previous studies, especially in Chinese short texts. To bridge this gap, we propose the P-BiLSTM-SA model, which integrates personalities into sentiment classification by combining BiLSTM and self-attention mechanisms. We grouped Weibo texts based on personalities and constructed a personality lexicon using the Big Five theory and clustering algorithms. Separate sentiment classifiers were trained for each personality group using BiLSTM and self-attention, and their predictions were combined by ensemble learning. The performance of the P-BiLSTM-SA model was evaluated on the NLPCC2013 dataset and showed significant accuracy improvements. In particular, it achieved 82.88% accuracy on the NLPCC2013 dataset, a 7.51% improvement over the baseline BiLSTM-SA model. The results highlight the effectiveness of incorporating personality factors into sentiment classification of short texts.

An Aggregator Framework for Transforming Big Data in Real-Time using PT-INDRNN

The prediction of stock market prices based on the financial text sentiment classification using Machine Learning (ML) and Deep Learning (DL) models is becoming popular among researchers in the era of Big Data (BD). Nevertheless, owing to the lack of extensive analysis, most of the developed ML and DL models failed to achieve better classification results. Thus, for the real-time prediction of the polarity of the stock price, a Probability Tanh-Independently Recurrent Neural Network (PT-IndRNN)-based classification of the sentiment of the financial text data of Twitter is proposed to solve this problem. Primarily, by employing the corresponding API, the real-time financial data and Twitter data are extracted and stored in the MongoDB database using Apache Flume. This stored data with the historical big datasets are taken and pre-processed. Next, by deploying the proposed Hadoop Distributed File System (HDFS) clustering, the pre-processed stock market data and Twitter data in real-time, as well as the historical dataset, are combined separately. After that, the features are extracted from the clustered sentences. Then, by utilizing the Senti Word Net, the sentences chosen using Linear Scaling-Dwarf Mongoose Optimization Algorithm (LS-DMOA) are converted to negative and positive scores. In the end, the sentiment of the financial texts is classified by the PTh-Ind RNN, which is proved by obtaining reliable result values.

Sentiment Classification of Social Network Text Based on AT-BiLSTM Model in a Big Data Environment

To tackle the challenge of ineffective sentiment prediction using current sentiment classification methods, this paper introduces a method social network text sentiment classification. The method leverages a bidirectional short and long-term memory model (AT-BiLSTM), specifically designed for a big data environment. First, a vectorized representation of text is realized by introducing a pre-trained BERT model, and the classification results are dynamically adjusted according to the semantic information of the words. Then, the BiLSTM combined with the attention mechanism performs aspect-level sentiment analysis, and the corresponding model AT-BiLSTM is formulated. Finally, the BERT model randomly selects input tags for information masking and pre-trains the proposed model. The proposed method was evaluated against three alternative methods using an identical dataset. The results show that the novel method achieved the highest accuracy, recall, and F1-score, reaching 93.72%, 93.91%, and 92.38%, respectively. Consequently, the proposed method demonstrates superior performance compared to the other three methods evaluated.

DocOps: LSTM-Based Text Sentiment Classification for Evaluating Patient Reviews and Doctor Selection

DocOps: LSTM-Based Text Sentiment Classification for Evaluating Patient Reviews and Doctor Selection

New algorithm for determining the number of features for the effective sentiment-classification of text documents

Sentiment analysis of text documents is a very important part of contemporary text mining. The purpose of this article is to present a new technique of text sentiment analysis which can be used with any type of a document-sentiment-classification method. The proposed technique involves feature selection independently of a classifier, which reduces the size of the feature space. Its advantages include intuitiveness and computational noncomplexity. The most important element of the proposed technique is a novel algorithm for the determination of the number of features to be selected sufficient for the effective classification. The algorithm is based on the analysis of the correlation between single features and document labels. A statistical approach, featuring a naive Bayes classifier and logistic regression, was employed to verify the usefulness of the proposed technique. They were applied to three document sets composed of 1,169 opinions of bank clients, obtained in 2020 from a Poland-based bank. The documents were written in Polish. The research demonstrated that reducing the number of terms over 10-fold by means of the proposed algorithm in most cases improves the effectiveness of classification.

A Study of Knowledge Discovery and Pattern Recognition Based on Large-Scale Sentiment Data in Online Education for College Students

With the development of the internet, online education platforms have become the main means of acquiring knowledge for people. To improve the efficiency of user analysis, this paper proposes a framework for user sentiment recognition based on course review information of online education platforms. Firstly, according to the characteristics of the review data of online platforms, the authors use crawlers and open-source word separation tools to complete data collection and collation; secondly, the authors use TextCNN (text convolutional neural networks) and BILSTM (bi-long-short-term-memory) to combine to build a feature layer fusion text sentiment classification model. The results show that the average precision of the model built is 95.1% for the three sentiment classifications. Finally, a test of the reviews of new courses on online education platforms is conducted. The sentiment recognition rate of all three types of new courses exceeds 90%. Therefore, the proposed model provides new ideas for user analysis.

Commemoration and Emotional Sustenance: A Text Analysis of Students' Motivations Behind Online Mourning of Dr Li Wenliang.

As one of the first doctors issued a protective warning to the public, Dr. Li Wenliang was known as "whistleblower" of COVID-19 pandemic. After his death of COVID-19, students entered to his Sina Weibo to display their condolences and sorrow. We conduct text analysis and sentiment classification to investigate the motivation behind online mourning for Dr. Li among students on Sina Weibo. Our results indicate that, a) there always more than one motivation behind online mourning exists in each time period. b) continuing connection and semi-interaction with the deceased is the main motivation when students mourn online. c) there exists positive correlation between the influence of the deceased and the motivation--sharing information with the community of fans and creating social support in a time of loss and social support. d) the motivation--honoring the dead and expressing sadness and resentment can gradually lose over time.

Short Text Sentiment Classification Using Bayesian and Deep Neural Networks

The previous multi-layer learning network is easy to fall into local extreme points in supervised learning. If the training samples sufficiently cover future samples, the learned multi-layer weights can be well used to predict new test samples. This paper mainly studies the research and analysis of machine short text sentiment classification based on Bayesian network and deep neural network algorithm. It first introduces Bayesian network and deep neural network algorithms, and analyzes the comments of various social software such as Twitter, Weibo, and other popular emotional communication platforms. Using modeling technology popular reviews are designed to conduct classification research on unigrams, bigrams, parts of speech, dependency labels, and triplet dependencies. The results show that the range of its classification accuracy is the smallest as 0.8116 and the largest as 0.87. These values are obtained when the input nodes of the triple dependency feature are 12,000, and the reconstruction error range of the Boltzmann machine is limited between 7.3175 and 26.5429, and the average classification accuracy is 0.8301. The advantages of triplet dependency features for text representation in text sentiment classification tasks are illustrated. It shows that Bayesian and deep neural network show good advantages in short text emotion classification.

A semi-supervised short text sentiment classification method based on improved Bert model from unlabelled data

Short text information has considerable commercial value and immeasurable social value. Natural language processing and short text sentiment analysis technology can organize and analyze short text information on the Internet. Natural language processing tasks such as sentiment classification have achieved satisfactory performance under a supervised learning framework. However, traditional supervised learning relies on large-scale and high-quality manual labels and obtaining high-quality label data costs a lot. Therefore, the strong dependence on label data hinders the application of the deep learning model to a large extent, which is the bottleneck of supervised learning. At the same time, short text datasets such as product reviews have an imbalance in the distribution of data samples. To solve the above problems, this paper proposes a method to predict label data according to semi-supervised learning mode and implements the MixMatchNL data enhancement method. Meanwhile, the Bert pre-training model is updated. The cross-entropy loss function in the model is improved to the Focal Loss function to alleviate the data imbalance in short text datasets. Experimental results based on public datasets indicate the proposed model has improved the accuracy of short text sentiment recognition compared with the previous update and other state-of-the-art models.