Chinese Micro-blog Sentiment Analysis Based on SVM and Complex Phrasing

Aiming at the problem that normal vector representation method cannot fully represent the emotional semantic information contained in the text when dealing with Chinese text sentiment analysis task, a multi-granularity convolutional capsule neural network model is constructed. The input of sentiment analysis model is vector representation of the texts trained through the language model. As there exists the problem that a single language model is not enough to abstract text features, a multi-granularity text vector representation method based on BERT is proposed. The text vector representations with different granularities are input into the improved multi-channel convolutional capsule neural network. The capsule layer can associate the low-level and high-level text features, and it will extract information selectively through the dynamic routing algorithm to construct emotional and semantic features of the whole text. Multiple comparative experiments confirm that the method proposed in this paper will efficiently improve the accuracy of Chinese sentiment analysis.

In recent years, vocabulary emotion processing has become immensely popular and the requirements for language emotion analysis mining and processing have become significantly abundant. The sentiment extraction and analysis work has always been very challenging; especially, the Chinese word segmentation operation is difficult to deal with effectively, the multiple combinations of implicit and explicit words make the task of sentiment analysis mining more difficult, and, in particular, the efficiency of machine analysis of language sentiment is feeble. We use some expressions and sentiment vocabulary dictionaries combined with hybrid structures and use information synergy methods to get in touch with sentiment analysis methods. We use the relevant sentiment to evaluate the explicit or implicit emotional association of the emotional connection of the vocabulary and add the unique emotional word matrix to analyze the related clustering results of the emotional words to continuously optimize and upgrade the performance, so that our sentiment analysis results are systematic in terms of efficiency and significantly improved.

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.

In recent years, there has been significant progress in Chinese text sentiment analysis research. However, few studies have investigated the differences between languages, the effectiveness of domain knowledge, and the requirements of downstream tasks. Considering the uniqueness of Chinese text and the practical needs of sentiment analysis, this study aims to address these gaps. To achieve this, we propose a method that deeply integrates the knowledge vector obtained from the emotional knowledge triplets using the TransE model with feature vectors from models like BiGRU and attention mechanisms. We also introduce radical features and emotional part of speech features based on the characteristics of characters and words. In addition, we propose a collaborative approach that integrates characters, words, radicals, and multi-granularity semantic features such as part of speech. Our approach, as evidenced by the Douban Film Review dataset and the NLPECC dataset, proficiently leverages emotional insights alongside nuanced linguistic elements, significantly bolstering the accuracy of sentiment detection in Chinese texts. The method achieved F1-score of 89.23% and 84.84%, respectively, underscoring its efficacy in the realm of Chinese sentiment analysis.

The long text in our study involves a complete discourse structure with a title and approximately 1500 Chinese characters within. Long text sentiment analysis has faced a lot of difficulties on the grounds that a long text often comprises multiple sentiments and diverse focuses. This paper proposes to analyze the sentiment of long texts based on the combination of title and topic sentence. Based on the fine-labeled texts, the analysis extracted the important features of topic sentences, such as location, feature words, degree of topic relevance and emotional words. Then two topic sentences were extracted from each text that can best represent the topic through weighted calculation of these multi-dimensional features. Next this paper combined the two topic sentences with the title to complete the sentiment analysis of the whole document. The topic sentence extraction method has achieved good results in the task of extracting and judging key emotional sentences of news in the 6th COAE (Chinese Opinion Analysis Evaluation) (2014), which shows that the method is effective. This method has been put into practice in the National Language Public Opinion Monitoring system, with an accuracy of 0.82.

Topic-related Chinese message sentiment analysis

This study investigates the presentation and distribution of emotion words in beginner-level Spanish L2 textbooks, focusing on the presence of positivity bias and the types of emotion words included. Previous research has indicated that positive words are learned earlier and used more frequently than negative words, leading to the hypothesis that L2 pedagogical materials might reflect this trend. Four widely-used beginner Spanish textbooks from major publishers were analyzed to determine the frequency and types of emotion words presented. The analysis revealed that emotion words constitute a small portion of the total vocabulary in these textbooks, with only 2.5% of the words being emotion-related. Both positive and negative emotion words were equally represented, with 32 types of each, and their occurrences were comparable, suggesting no significant positivity bias in the textbooks. This balanced distribution of emotional words is beneficial for L2 learners, providing them with a comprehensive range of emotional expressions necessary for effective communication. The study also examined the thematic focus and progression of emotion vocabulary across the textbooks. Common themes such as health and well-being, leisure and modern life, and family and relationships were identified as contexts where emotion words are frequently introduced. The textbooks generally introduced basic emotion words early in the course, with a gradual increase in complexity as students progressed. This pedagogical approach ensures that learners build a solid foundation of emotion vocabulary, which is expanded upon as they advance in their studies. A detailed semantic analysis of the emotion words revealed that positive emotion words exhibited more semantic uniformity, often related to the basic concept of happiness. In contrast, negative emotion words displayed a wider range of semantic traits, including complex emotions such as boredom, anxiety, jealousy, and hatred. This diversity in negative emotion vocabulary suggests that the textbooks effectively introduce various negative emotions appropriate for beginner-level learners. The findings of this study have several pedagogical implications. First, the balanced presentation of positive and negative emotion words in textbooks supports the development of comprehensive emotional expression skills in L2 learners. Educators are encouraged to create supportive learning environments where students can practice using emotion vocabulary in context, through activities such as role-playing, discussions, and writing exercises. Additionally, the study highlights the importance of cultural context in teaching emotion words, suggesting that instructors should incorporate cultural differences in emotional expression to enhance learners' understanding and avoid misunderstandings. Furthermore, the study suggests that traditional one-to-one translation methods in textbooks may not fully capture the nuances of emotion vocabulary. To address this, educators should provide authentic contexts and scenarios for students to practice emotion words, helping them grasp the cultural meanings and differences between L1 and L2. The use of language learning apps and authentic materials such as movies and videos can also enhance the learning experience by providing real-life examples of emotional expression. In conclusion, this study provides valuable insights into the presentation of emotion words in beginner Spanish L2 textbooks, highlighting the need for balanced and contextually rich pedagogical approaches to support the development of emotional vocabulary in L2 learners. Future research should expand the analysis to a larger number of textbooks to validate these findings and explore additional strategies for teaching emotion words effectively.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0731/a.php" alt="Hit counter" /></p>

Implicit sentiment analysis (ISA) can be distinguished from traditional text sentiment analysis by the fact that it does not rely on emotional words as emotional clues, and the expression is usually vaguer. Identifying implicit emotions is more difficult, as it requires a deeper understanding of the context, even when emotional words are absent. Researchers have focused on context feature modeling and developing sophisticated feature extraction mechanisms instead of starting from the emotional perspective. Enhancing the difference in emotional features of text samples is an intuitive method to address this challenge. We proposed a supervised contrastive learning (SCL) method during training that enables the model to conduct contrastive learning based on emotion labels even while training on weak emotion features. SCL training can strengthen the average embedding distance between texts with different emotion labels and enhance implicit emotion discrimination. Moreover, research indicates that contextual information can improve implicit emotion classification ability. Therefore, we applied a straightforward context feature fusion method (bi-affine) over a more complicated context feature modeling approach. To evaluate the effectiveness of our proposed method, we conducted experiments on the SMP2019-ECISA (Chinese implicit sentiment analysis) dataset. The results show a 2.13% enhancement in the F1 value compared to the BERT baseline, proving the effectiveness of our methods.

Chinese short text sentiment analysis can help understand society’s views on various hot topics. Many existing sentiment analysis methods are based on sentiment dictionaries. Still, sentiment dictionaries are easily affected by subjective factors. They require a lot of time to build as well as maintenance to prevent obsolescence. For the aim of extracting rich information within texts more effectively, we propose a Channel Attention TextCNN with Feature Word Extraction model (CAT-FWE). The feature word extraction module helps us choose words that affect the sentiment of reviews. Then, these words are integrated with multi-level semantic information to enhance the information of sentences. In addition, the channel attention textCNN module that is a promotion of traditional TextCNN tends to pay more attention to those meaningful features. It eliminates the impacts of features that do not make any sense effectively. We apply our CAT-FWE model to both fine-grained classification and binary classification tasks for Chinese short texts. Experiment results show that it can improve the performance of emotion recognition.

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.

In the last few years, with rapid growth in use of networking sites such as twitter and Facebook has been increased greatly. This also attracted the researcher to use social networks data for sentiment analysis. Sentiment analysis is also known as opinion mining is the process of finding out the emotion such as positive, negative and neutral from the series of words. In present, on internet huge amount of data has been generated and to extract useful information from data is also become interest for the researchers. Sentiment analysis has been done mostly in English and Chinese languages. In this paper, sentiment classification is done on Urdu news tweets. The proposed methodology consists upon two steps. In first step data preprocessing is done such as removal of hash tag and removal of stop words is done. In second step feature vector is designed. The feature vector is formulated by through the identification of number of positive words, negative words, and presence of negation and use of POS tags. After formulation of feature vector the decision tree is used as classification algorithm. The decision tree classifies the tweet as positive, negative and neutral. The experimental result of the proposed methodology shows significant success in terms of accuracy and sentiment analysis.

Nowadays, classifying sentiment from social media has been a strategic thing since people can express their feeling about something in an easy way and short text. Mining opinion from social media has become important because people are usually honest with their feeling on something. In our research, we tried to identify the problems of classifying sentiment from Indonesian social media. We identified that people tend to express their opinion in text while the emoticon is rarely used and sometimes misleading. We also identified that the Indonesian social media opinion can be classified not only to positive, negative, neutral and question but also to a special mix case between negative and question type. Basically there are two levels of problem: word level and sentence level. Word level problems include the usage of punctuation mark, the number usage to replace letter, misspelled word and the usage of nonstandard abbreviation. In sentence level, the problem is related with the sentiment type such as mentioned before. In our research, we built a sentiment classification system which includes several steps such as text preprocessing, feature extraction, and classification. The text preprocessing aims to transform the informal text into formal text. The word formalization method in that we use is the deletion of punctuation mark, the tokenization, conversion of number to letter, the reduction of repetition letter, and using corpus with Levensthein to formalize abbreviation. The sentence formalization method that we use is negation handling, sentiment relative, and affixes handling. Rule-based, SVM and Maximum Entropy are used as the classification algorithms with features of count of positive, negative, and question word in sentence and bigram. From our experimental result, the best classification method is SVM that yields 83.5% accuracy.

Sentiment analysis is a crucial part of natural language processing (NLP) that categorizes text into positive, negative, or neutral sentiments. This technology helps businesses automatically interpret customer emotions, enabling informed decisions in marketing, product development, and customer support. Traditional machine learning models like Random Forest, Naive Bayes, and Support Vector Machine (SVM) have been commonly used for sentiment classification, especially effective for short and concise texts such as brief customer comments. SVM, in particular, performs well in classifying sentiments in short texts. However, the emergence of advanced large language models (LLMs) like GPT-4 has revolutionized sentiment analysis by capturing more nuanced emotional expressions. GPT-4 excels in understanding complex context, sarcasm, and subtle emotional tones within longer texts, offering multi-dimensional sentiment insights such as satisfaction, frustration, and uncertainty. This makes it more capable than traditional models when analyzing detailed customer reviews or conversations. Comparative studies reveal that while traditional models handle short texts efficiently, GPT-4 surpasses them in analyzing in-depth content with greater precision, recall, and F1 scores. GPT-4 also uniquely identifies mixed sentiments that simpler models often miss. In conclusion, although traditional machine learning approaches remain useful for straightforward sentiment analysis tasks, GPT-4 provides a more sophisticated and comprehensive understanding of customer emotions in complex texts. Integrating GPT-4 into sentiment analysis workflows can significantly enhance the accuracy and richness of insights derived from customer feedback, supporting better business decisions.

Implicit sentiment analysis is an important part of sentiment computing, especially sentiment analysis based on deep learning has become a research hotspot in recent years. In this paper, convolutional neural networks are used to extract text features, combined with long-term and short-term memory network (LSTM) structure to extract context information, and add attention mechanism to the network to construct a new hybrid neural network model to achieve implicit emotion for text analysis. The hybrid neural network model extracts more meaningful sentence semantic and structural hidden features from the word-level and sentence-level hierarchies, respectively, and pays attention to the features with a large emotional contribution rate through the attention mechanism. The classification accuracy rate of the model on the public implicit sentiment dataset has reached 77%. The research of implicit sentiment analysis can improve the effect of text sentiment analysis more comprehensively, and further promote the application of text sentiment analysis in the fields of knowledge embedding, text representation learning, user modeling and natural language.

The influence of emotion on attention has been examined more closely in recent years using a variety of paradigms. Öhman (1993) suggested that participants more readily pay attention to negative information than neutral information. The current work was designed to expand upon these findings by examining both negative and positive emotion word processing in a non-clinical population using the dot probe task. Experiments 1a and 1b examined the supraliminal and subliminal processing of negative and positive emotion words presented in separate blocks. The results revealed that participants responded faster to the probe when it appeared in the same location as the negative emotion word relative to when it appeared in the same location as an emotionally neutral word, but were equally fast at responding to the probe when it appeared in the same location as the positive emotion word or neutral word. These effects occurred regardless of whether the words were presented with or without a mask. These findings suggest that negative emotion words automatically capture attention even for non-clinical samples and even when they are presented below the threshold of conscious awareness, indicating that this is a robust and automatic effect. In Experiments 2a and 2b, the negative and positive words were intermixed within the same block. When the words were masked, the results revealed faster processing of the probe when it appeared in the same location as either a negative word or a positive word, relative to when it appeared in the same location as a neutral word; however, when the words were presented without a mask, the probe was processed equally fast regardless of whether it appeared in the same location as a negative or positive emotion word or a neutral word. The findings from Experiment 2b also indicate that positive words have the ability to automatically capture attention and influence subsequent processing when they are presented below the threshold of awareness. In addition, the hemispheric processing of the two word types was also examined. The results from Experiments 1a and 1b supported the Right Hemisphere Hypothesis (RHH), indicating that the negative and positive words were processed more efficiently in the left visual field (LVF). In Experiments 2a and 2b, the RHH was supported when the words were masked, but the Valence Specific Hypothesis (VSH) was supported when the words were presented without a mask. Specifically, the positive words were processed more efficiently in the right visual