Comments-Attached Chinese Microblog Sentiment Classification Based on Machine Learning Technology

Sentiment analysis of Chinese micro-blog based on sentiment dictionary has become a challenging research subject in the field of artificial intelligence. However, due to insufficient sentiment words, Chinese micro-blog sentiment analysis is difficult to process high accuracy. Aimed at this issue, we propose a method for constructing multiple sentiment dictionaries, which mainly constructs original sentiment dictionary, emoji dictionary, and other related dictionaries. Among them, we have innovatively constructed a Chinese micro-blog new word sentiment dictionary. Multiple sentiment dictionaries increase the coverage of sentiment words. At the same time, we further analyze semantic rule sets between Chinese micro-blog texts and take the inter-sentence analysis rules and sentence pattern analysis rules into the sentiment analysis of Chinese micro-blog, which further improves the accuracy of Chinese micro-blog sentiment analysis. Finally, based on the method of multiple sentiment dictionaries and semantic rule sets, we propose an algorithm for Chinese micro-blog sentiment calculation from complex sentences to clauses and then from clauses to words, and finally combined with the emoji. This algorithm can accurately classify Chinese micro-blog into positive Chinese micro-blog, negative Chinese micro-blog, and neutral Chinese micro-blog. The experimental results show that this method has greatly improved the sentiment analysis of Chinese micro-blog.

Now micro-blog media is growing fast and micro-blog short text has also become a new type of information carrier. User’s sentiment orientation and emotion of the topic or event in a large number of user’s micro-blog, can not only provide decision-making basis in business but also provide support for government's public opinion monitoring. During micro-blog emotion classification, characteristic information is extracted directly influences the classification effect. This paper uses emotional sentences, emotional symbol, emotional word polarity and other emotional information as classification feature, and use NLP&CC Chinese micro-blog sentiment analysis evaluation standard segmentation of emotion in the polarity based emotion. This paper proposed the Chinese micro-blog sentiment classification based on the feature of amorous feeling. Parallel tests suggested that this method has better classification results, and has verified when micro-blog text’s emotional level is higher, the effect of the method is better.

The rapid dissemination of information related to the K-Pop world, facilitated by social media, has made it easier to follow developments and controversies. One notable case that sparked extensive discussion on Twitter was the bullying allegations against Kim Garam of LE SSERAFIM. Researchers, using Twitter data, sought to analyze Indonesian public sentiment regarding this case through sentiment analysis, which classifies opinions as positive or negative. For processing textual data, text mining methods, particularly classification techniques, are employed. Two popular algorithms in text mining are the Naive Bayes classifier and the support vector machine (SVM). The Naive Bayes classifier is favored for its speed, simplicity, and high accuracy, while the SVM excels at identifying a hyperplane that maximizes the margin between classes. In this study, sentiment classification results were labeled as either positive or negative. The comparison between the Naive Bayes classifier and SVM for classifying responses to Kim Garam's bullying case on Twitter showed high accuracy rates: 93% for Naive Bayes and 97% for SVM. The higher accuracy of the SVM algorithm indicates its superiority over the Naive Bayes classifier in this context.

Microblog sentiment classification is an interesting and important research topic with wide applications. Traditional microblog sentiment classification methods usually use a single model to classify the messages from different users and omit individuality. However, microblogging users frequently embed their personal character, opinion bias and language habits into their messages, and the same word may convey different sentiments in messages posted by different users. In this paper, we propose a personalized approach for microblog sentiment classification. In our approach, each user has a personalized sentiment classifier, which is decomposed into two components, a global one and a user-specific one. Our approach can capture the individual personality and at the same time leverage the common sentiment knowledge shared by all users. The personalized sentiment classifiers of massive users are trained in a collaborative way based on multi-task learning to handle the data sparseness problem. In addition, we incorporate users' social relations into our model to strengthen the learning of the personalized models. Moreover, we propose a distributed optimization algorithm to solve our model in parallel. Experiments on two real-world microblog sentiment datasets validate that our approach can improve microblog sentiment classification accuracy effectively and efficiently.

The field of sentiment mining and text summarization has evoked the interest of many scientists and researchers over the last few years, as the textual data has become useful for many real-world applications and challenges. Sentiment Analysis and Opinion Mining is the most popular field for analyzing and discovering insights from text data from various sources, such as Facebook, Twitter and Amazon, Zomato, etc. It involves a computational study of an individual's behavior in terms of buying interest and then extracting his opinions on the business entity of the company. This entity can be viewed as an event, individual, blog post or product experience. Scholars in the fields of natural language processing, data mining, machine learning and others have tested a variety of methods for automating sentiment analysis. These reviews are increasing on a daily basis, as a result of which the summarization of the reviews plays a role where the text is summarized as needed, which provides useful information from a large number of reviews. It is very difficult for a human being to extract and interpret useful data from a very large file. In the text analysis, the value of sentences is decided on the basis of the linguistic characteristics of sentences. This paper provides a comprehensive review of current and past work on sentiment analysis and text description. In this research work, a new hybrid classification system is proposed based on coupling classification methods using arcing classifiers and their quality is evaluated within terms of accuracy. The Classifier Collection was constructed using Naïve Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA). The proposed work consists of a comparative study of the efficacy of the ensemble technique for sentiment classification. The feasibility and benefits of the proposed approaches are demonstrated by a restaurant review that is widely used in the field of sentiment classification. A wide range of comparative studies is performed and, ultimately, some in-depth analysis is addressed and conclusions are drawn on the efficacy of the ensemble technique for sentiment classification.

BackgroundTo harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content.ObjectivesThe objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid–related tweets.MethodsTweets were collected using Twitter streaming Application Programming Interface and filtered through the eDrugTrends platform using keywords related to cannabis, marijuana edibles, marijuana concentrates, and synthetic cannabinoids. After creating coding rules and assessing intercoder reliability, a manually labeled data set (N=4000) was developed by coding several batches of randomly selected subsets of tweets extracted from the pool of 15,623,869 collected by eDrugTrends (May-November 2015). Out of 4000 tweets, 25% (1000/4000) were used to build source classifiers and 75% (3000/4000) were used for sentiment classifiers. Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machines (SVM) were used to train the classifiers. Source classification (n=1000) tested Approach 1 that used short URLs, and Approach 2 where URLs were expanded and included into the bag-of-words analysis. For sentiment classification, Approach 1 used all tweets, regardless of their source/type (n=3000), while Approach 2 applied sentiment classification to personal communication tweets only (2633/3000, 88%). Multiclass and binary classification tasks were examined, and machine-learning sentiment classifier performance was compared with Valence Aware Dictionary for sEntiment Reasoning (VADER), a lexicon and rule-based method. The performance of each classifier was assessed using 5-fold cross validation that calculated average F-scores. One-tailed t test was used to determine if differences in F-scores were statistically significant.ResultsIn multiclass source classification, the use of expanded URLs did not contribute to significant improvement in classifier performance (0.7972 vs 0.8102 for SVM, P=.19). In binary classification, the identification of all source categories improved significantly when unshortened URLs were used, with personal communication tweets benefiting the most (0.8736 vs 0.8200, P<.001). In multiclass sentiment classification Approach 1, SVM (0.6723) performed similarly to NB (0.6683) and LR (0.6703). In Approach 2, SVM (0.7062) did not differ from NB (0.6980, P=.13) or LR (F=0.6931, P=.05), but it was over 40% more accurate than VADER (F=0.5030, P<.001). In multiclass task, improvements in sentiment classification (Approach 2 vs Approach 1) did not reach statistical significance (eg, SVM: 0.7062 vs 0.6723, P=.052). In binary sentiment classification (positive vs negative), Approach 2 (focus on personal communication tweets only) improved classification results, compared with Approach 1, for LR (0.8752 vs 0.8516, P=.04) and SVM (0.8800 vs 0.8557, P=.045).ConclusionsThe study provides an example of the use of supervised machine learning methods to categorize cannabis- and synthetic cannabinoid–related tweets with fairly high accuracy. Use of these content analysis tools along with geographic identification capabilities developed by the eDrugTrends platform will provide powerful methods for tracking regional changes in user opinions related to cannabis and synthetic cannabinoids use over time and across different regions.

Text sentiment analysis technology is a hot topic recently. As a short text, there is a feature of using complex sentences to express the author's true views and complex emotional tendencies in micro-blogs. In current researches on sentiment classification based on machine learning, few of them focus on complex sentences. This paper proposed a sentiment analysis method based on SVM and complex phrasing classifier, and made a full analysis of structural features of Chinese conditional sentences, transition sentences and multiple negative sentences, which were taken as text features. A variety of different combinations of features were chosen, including emotional words, speech, negative words, the degree of adverbs and punctuation, etc., to optimize the results of sentiment analysis through multiple sets of experiments. The experiments show that when we choose the combinations of features of emotional words, part of speech and complex sentence patterns, this method improved the accuracy of sentiment classification compared to the common method.

Audio sentiment analysis is a rapidly evolving field that seeks to uncover emotional insights from audio signals, with broad applications in technology, healthcare, and user experience design. This paper presents a detailed review of recent advancements, highlighting key techniques, ongoing challenges, and real-world applications that illustrate the potential of this technology. The Literature Review provides an integrated analysis of significant findings across various studies, identifying core trends and unique contributions. Finally, the Conclusion explores promising future research directions, laying the groundwork for continued innovation in extracting sentiment from sound. Keywords Audio Sentiment Analysis, Sentiment Classification, Deep Learning Techniques, Feature Extraction, Transfer Learning Ensemble Methods, Multi-Task Learning, Challenges in Sentiment Analysis, Applications of Sentiment Analysis, Literature Review of Audio Sentiment Analysis

Modality translation-based multimodal sentiment analysis under uncertain missing modalities

Purpose: This paper provides a comprehensive review of current methods, progress, and open challenges in Sentiment Analysis (SA)—a computational discipline for extracting and interpreting opinions, sentiments, and emotions from text. It aims to identify the limitations of existing approaches and explore pathways toward more generalizable, explainable, and ethically robust sentiment analysis models. Design/Methodology/Approach: The study synthesizes insights from a tertiary review (Lighart et al., 2021), which aggregates findings from 14 systematic literature reviews and mapping studies, and a domain-specific review (Sweta, 2024) focusing on SA in educational contexts. A thematic analysis integrates methodological evolution, major challenges, and emerging trends to produce a consolidated overview of the field’s trajectory. Findings: Results indicate a clear shift from lexicon-based and traditional machine learning approaches to deep learning and transformer-based architectures (e.g., LSTM, CNN, BERT, GPT). Despite significant progress, persistent issues remain—such as domain and language dependency, contextual subtlety (sarcasm, irony), data imbalance, lack of explainability, and ethical concerns regarding privacy. New trends, including cross-domain transfer learning, explainable AI (XAI), multimodal sentiment analysis, and real-time processing, hold promise for overcoming these barriers. Research Limitations/Implications: The review highlights the need for standardized datasets, cross-lingual benchmarks, and interdisciplinary collaboration between NLP researchers and domain experts to enhance model robustness and ethical compliance. Originality/Value: By integrating high-level evidence from tertiary research with practical insights from domain-specific studies, this paper outlines the current landscape and future directions for building generalizable, transparent, and ethically grounded sentiment analysis systems.

Sentiment analysis can be used to gain insight about the views of customers to make business decisions better as well as look into upcoming trends. In this study, the analysis of Amazon product reviews through the use of machine learning algorithms is used to determine the sentiment of customers and also to identify the trend in future. The pre-processing methods, such as text cleaning, tokenizing, and lemmatizing are used in such study and in this work used oversampling and under sampling to balance the classes. Sentiment forecasting, deals with missing values and resampling time-series data and normalizing sentiment scores. The TF-IDF is part of the feature extraction in classification and more time-series features enhance the forecast. It splits the data into the ratio of 80-20 split in order to evaluate the same. The sentiment classification in this study has employed Support Vector Machine (SVM), Naive Bayes (NB), and Logistic Regression (LR) with their accuracies being 89 percent 87 percent and 88 percent respectively and SVM gave the best accuracy. Sentiment forecasting is carried out with the help of an LSTM model, which proves to be excellent in the capture of how things evolve with time. A real-time sentiment analysis interactive application is developed and users can classify reviews and even evaluate how models perform. This research pinpoints the positivity of machine learning in sentiment analysis use and how it is applied in the real world.

Sentiment Analysis of Self Driving Car Dataset: A comparative study of Deep Learning approaches

Documents with complex sentiment expressions generally pose great challenges in sentiment analysis. This paper proposes a statistical framework to improve sentiment classification within multiscale sentences or paragraphs. A Set of Sentiment Parts (SSP) is first introduced to express sentiment features in different contexts of varying scales. A statistic combination is then determined by analyzing canonical correlations on Riemannian manifolds. A metric learning method is designed to keep the orthogonality within Riemannian point pairs. The nearest neighbor (NN) method is finally used to classify sentiments of SSP. Promising results on various sentiment analysis data sets demonstrate the effectiveness of the proposed method.

This study addresses challenges in sentiment analysis for low-resource educational contexts by proposing a framework that integrates Few-Shot Learning (FSL) with Transformer-based ensemble models and boosting techniques. Sentiment analysis of student feedback is crucial for improving teaching quality, yet traditional methods struggle with data scarcity and computational inefficiency. The proposed framework leverages self-attention mechanisms in Transformers and combines models through Gradient Boosting to enhance performance and generalization with minimal labeled data. Evaluated on the UIT-VSFC dataset, comprising Vietnamese student feedback, the framework achieved superior F1-scores in sentiment and topic classification tasks, outperforming individual models. Results demonstrate their potential for extracting actionable insights to enhance educational experiences. Despite its effectiveness, the approach faces limitations such as reliance on pre-trained models and computational complexity. Future work could optimize lightweight models and explore applications in other domains like healthcare and finance.

Insurance Administration Organization, which can be used by all people. However, this organization has received various criticisms from the public through social media, namely Twitter. This study aims to analyze public sentiment about the Indonesian Social Insurance Administration Organization on Twitter. The method used in this research is the Naive Bayes Classifier (NBC) method and uses the Support Vector Machine (SVM) method as a comparison. The amount of data used was 12,990 tweets with a data collection period from September 14, 2019 - February 18, 2020. The study compared the two classifier models built, namely the classifier model with two sentiment classes and four sentiment classes. The accuracy results show that the SVM method has a better accuracy value than the NBC method. SVM has an accuracy value of 63.60% and 82.77% for the two sentiment classes in the four sentiment classifier model. The tweet classification results show that the public's conversation about the Indonesian Social Insurance Administration Organization on Twitter has a negative polarity value tendency.