Revolutionizing Chinese sentiment analysis: A knowledge-driven approach with multi-granularity semantic features

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.

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.

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.

Topic-related Chinese message sentiment analysis

Aspect-level sentiment analysis can provide more detailed information than general sentiment analysis, because it aims to predict the sentiment polarities of the given aspects or entities in text. The state-of-the-art neural models use RNN with attention seems a good method for the characteristics of this task in English, but because of the complexity of Chinese texts and the difference between Chinese and English, English learning outcomes cannot be directly applied to Chinese. After re-examining the drawbacks of attention mechanism and obstacles that Chinese aspect-level sentiment analysis, we build upon line of research and propose three approaches overcome these issues. First, we propose a mixed vector to enhance the information embedded in Chinese word embedding. Second, we introduce an attention weight calculate method for target representation that better captures the semantic meaning of the opinion target. Third, we use CNN to extract local information in the text. The experimental results show that our model consistently outperforms the state-of-the-art methods on Chinese reviews.

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.

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 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.

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.

In Chinese sentiment analysis tasks, many existing methods tend to use recurrent neural networks (e.g., long short-term memory networks and gated recurrent units) and standard one-dimensional convolutional neural networks (1D-CNN) to extract features. This is because a recurrent neural network can deal with the order dependence of the data to a certain extent and the one-dimensional convolution can extract local features. Although these methods have good performance in sentiment analysis tasks, recurrent neural networks (RNNs) cannot be parallelized, resulting in time-inefficiency, and the standard 1D-CNN can only extract a single sample feature, with the result that the feature information cannot be fully utilized. To this end, in this paper, we propose a multichannel two-dimensional convolutional neural network based on interactive features and group strategy (MCNN-IFGS) for Chinese sentiment analysis. Firstly, we no longer use word encoding technology but use character-based integer encoding to retain more fine-grained information. Besides, in character-level vectors, the interactive features of different elements are introduced to improve the dimensionality of feature vectors and supplement semantic information so that the input matches the model network. In order to ensure that more sentiment features are learned, group strategies are used to form several feature mapping groups, so the learning object is converted from the traditional single sample to the learning of the feature mapping group, so as to achieve the purpose of learning more features. Finally, multichannel two-dimensional convolutional neural networks with different sizes of convolution kernels are used to extract sentiment features of different scales. The experimental results on the Chinese dataset show that our proposed method outperforms other baseline and state-of-the-art methods.

GP‐FMLNet: A feature matrix learning network enhanced by glyph and phonetic information for Chinese sentiment analysis

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.

Scene text information extraction plays an important role in many computer vision applications. Most features in existing text extraction algorithms are only applicable to one text extraction stage (text detection or recognition), which significantly weakens the consistency in an end-to-end system, especially for the complex Chinese texts. To tackle this challenging problem, we propose a novel text structure feature extractor based on a text structure component detector (TSCD) layer and residual network for Chinese texts. Inspired by the three-layer Chinese text cognition model of a human, we combine the TSCD layer and the residual network to extract features suitable for both text extraction stages. The specialized modeling for Chinese characters in the TSCD layer simulates the key structure component cognition layer in the psychological model. And the residual mechanism in the residual network simulates the key bidirectional connection among the layers in the psychological model. Through the organic combination of the TSCD layer and the residual network, the extracted features are applicable to both text detection and recognition, as humans do. In evaluation, both text detection and recognition models based on our proposed text structure feature extractor achieve great improvements over baseline CNN models. And an end-to-end Chinese text information extraction system is experimentally designed and evaluated, showing the advantage of the proposed feature extractor as a unified feature extractor.

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.

With the popularity of social media, opinion mining has gradually become a popular research field. Among these fields, sentiment analysis mining is an important research direction in the field of opinion mining. It aims to reveal the public's sentiment tendency, and attitude towards specific topics or events by analyzing text data generated by users on online platforms and digital media. However, the large amount of opinion data usually lacks effective annotation, which limits the learning and construction of opinion models. Therefore, focusing on the problem of the scarcity of labeled data in opinion analysis, this paper proposes a mining method for public opinion sentiment analysis based on multi-model fusion transfer learning, that can make full use of the limited labeled data to improve the learning efficiency of sentiment features by integrating the advantages of different models. Additionally, it introduces a transfer learning strategy to enable the models of the target domains to perform better in the absence of labeled data. Furthermore, the attention mechanism is combined to strengthen the acquisition of key features of the emotional colors and improve the accuracy of sentiment analysis. Specifically, the paper uses the ERNIE model to generate dynamic representations of the text word vectors in the dataset. It also uses TextCNN and BiGRU to construct a joint model for extracting local and overall features of the text word vectors. The parameters of the feature layer of the trained model are migrated to the target domain through transfer learning. The attention mechanism is combined with the model to identify the extreme elements of the sentiment. Finally, the local and overall features are fused to achieve comprehensive mining of public opinion and emotional information. This method can effectively improve the accuracy and generalization of public opinion analysis in cases of data scarcity. In the experimental part, the paper conducts comparisons and analyses in eight aspects: word embedding model, model combination, attention mechanism, transfer learning, source domain dataset, target domain dataset, model training, and baseline model. The four indicators, namely accuracy, precision, recall, and F1-measure are used to evaluate the performance of the method. The experiments are thorough and detailed, demonstrating the effective improvement of opinion mining performance.