Detecting ships from synthetic aperture radar (SAR) images is crucial in both military and civilian applications. In order to solve the problems of false and missed detection in complex scenes due to weak SAR image features, more small-size targets and large scale variations, we propose a context-guided and multi-scale fusion lightweight SAR ship detection model (CGMSF-SAR). First, the backbone network part incorporates the self-designed Multi-scale Group Convolutional Interaction Enhancement (MGCIE) module into the C2f module, which divides the input channels and assigns them to different sizes of convolutional kernels to capture various features across scales and reduce the computational redundancy. In the neck network part, an adaptive context-guided feature pyramid network (ACGFPN) is proposed, which captures the axial global context from horizontal and vertical directions through the pyramid context augmentation module and multiscale feature fusion module, explicitly models the rectangular key regions, and then uses dynamic interpolation fusion and multilayer recursive fusion to enhance the multiscale feature representation capability. Finally, a Dynamic Lightweight Detection Head (DLDH) is proposed to achieve efficient detection under low computation by GroupNorm to enhance the performance, shared convolution to reduce the parameters, and Scale layer to adapt to the target scale change. Experimental results on publicly available datasets show that the detection accuracy of CGMSF-SAR is significantly improved.

Multimodal sentiment analysis (MSA) is primarily used to detect the sentiment contained in different modalities. Existing research has overlooked the multi-scale information of image within multimodal data, leading to insufficient extraction of image features by the network, which consequently reduces the overall performance of the model. Inspired by Mamba’s powerful sequence modeling capabilities, we propose a Multi-Scale Sequence Fusion Model for Multimodal Sentiment Analysis. The model fuses image and text features at each image scale, treating the multiple fusion results as a complete sequence, and utilizes Mamba to capture the potential sequential sentiment features within this sequence across different modalities. Specifically, we propose the Local Fusion Module (LFM) and the Global Sequence Sentiment Module (GSSM). LFM enhances the module’s understanding of context information and spatial relationships by integrating image features of different scales with text information. GSSM treats the fused information from multiple LFMs as sequential information, improving the model’s ability to capture global context. Additionally, to help the sequence interaction between multi-scale image and text, we design the Dual-modal Transformation Module to facilitate the structural transformation of image and text features. Experiments on publicly available datasets demonstrate that our proposed model outperforms previous approaches, indicating its feasibility and effectiveness in the multimodal sentiment analysis task.

Personality detection identifies personality traits in text. Current approaches often rely on deep learning networks for text representation but they overlook the significance of psychological language knowledge in connecting user language expression to psychological characteristics. Consequently, the accuracy of personality detection is compromised. To address this issue, this paper presents PS-GCN, a model integrating Psychological knowledge and Sentiment semantic features through Graph Convolution Networks. Firstly, the Bi-LSTM network captures local features of preprocessed sentences to accurately represent the output of sentence sentiment features. Secondly, GCNs map psycholinguistic knowledge, forming semantic networks of entities and relationships. P-GCN is designed to capture the dependency information between psycholinguistic features, while S-GCN utilises syntactic structure analysis to gather more abundant information features and enhance semantic understanding ability. Finally, attention calculation is employed to reinforce key features and weaken irrelevant information. Additionally, a sentence group model captures combined features of related sentences, effectively utilising the text structure to mine sentimental features. Experimental results on multiple datasets demonstrate that the proposed method significantly improves the classification accuracy in personality detection tasks.

Patent Entity and Relation Extraction (PERE) aims to extract entities and entity-relation triples from unstructured patent texts. PERE is one of the fundamental tasks in patent text mining, providing crucial technical support for patent retrieval and technology opportunity discovery. Previous works struggle to capture the implicit semantic information hidden within overlapping triples, especially a large number of overlapping triples existing in patent texts. A Patent Entity and Relation Extraction model based on Context query and Axial attention is proposed, named PERE-CA. As for entity recognition, the text segment is regarded as candidate entity span and entity types are acquired by span classification. Subsequently, the semantic context related to an entity pair is calculated by a context query method. And the semantic context is integrated into entity pair representation. For relation extraction, axial attention is implemented to get the implicit semantic information among overlapping entity pairs. And then, the model outputs all valid entity-relation triples. Experimental results on the patent dataset TFH-2020 and the public dataset SciERC demonstrate that the implementation of context query and axial attention can effectively improve extraction performance.

Fake review detection has the characteristics of huge stream data processing scale, unlimited data increment, dynamic change, and so on. However, the existing fake review detection methods mainly target limited and static review data. In addition, deceptive fake reviews have always been a difficult point in fake review detection due to their hidden and diverse characteristics. To solve the above problems, this article proposes a fake review detection model based on sentiment intensity and PU learning (SIPUL), which can continuously learn the prediction model from the constantly arriving streaming data. First, when the streaming data arrive, the sentiment intensity is introduced to divide the reviews into different subsets (i.e., strong sentiment set and weak sentiment set). Then, the initial positive and negative samples are extracted from the subset using the marking mechanism of selection completely at random (SCAR) and Spy technology. Second, building a semi-supervised positive-unlabeled (PU) learning detector based on the initial sample to detect fake reviews in the data stream iteratively. According to the detection results, the data of initial samples and the PU learning detector are continuously updated. Finally, the old data are continually deleted according to the historical record points, so that the training sample data are within a manageable size and prevent overfitting. Experimental results show that the model can effectively detect fake reviews, especially deceptive reviews.

Sentiment classification can provide the decision support of social applications such as trend judgment, public opinion monitoring, etc. However, the accuracy of sentiment classification for Chinese Weibo is still not satisfactory due to the complexity of Chinese. In addition, affected by the different organisational structure levels, the sentiment tendency of fewer Weibo Comments may be judged to be the opposite. To solve the problem above, this paper presents a Chinese sentiment classification model based on extended sentiment dictionary and organisational structure of comments. First, the sentiment dictionary can be extended by using seven dictionaries, which include the base sentiment dictionary and six additional dictionaries. Then, the sets of rules are constructed, which include inter-sentence rules and organisational structure rules. Finally, comments on three hot topics are crawled and used to make the data sets for sentiment calculation. Accordingly, based on the result of sentiment calculation, sentiment classification is completed. The effectiveness of the proposed model is verified through comparison experiments, and the experimental results are also discussed.

Aspect-level sentiment classification, a significant task of fine-grained sentiment analysis, aims to identify the sentimental information expressed in each aspect of a given sentence The existing methods combine global features and local structures to obtain good classification results. However, the introduction of global features will bring noise and reduce the classification accuracy. To solve this problem, a new method is proposed, named GP-GCN. In our proposed method, the global feature is further simplified to reduce the noise . The local structures and global features obtained by orthogonal feature projection are introduced into aspect-level sentiment classification. First, the simplified global feature structures of text are built. Through orthogonal projection, GCN not only weakens the dependency of the graph node in updating process but also reduces the dependency between node and corpus. Next, syntactic dependency structure and sentence sequence information are utilised to mine the local dependency structure of sentences. A percentage-based multi-headed attention mechanism is proposed to measure the critical output of GCN, which can better represent sentences for given aspects. Finally, location coding is input to simulate aspect-specific representations between each aspect and its context such that the text becomes more discriminative in sentiment classification. The experimental results show that the proposed method effectively improves the accuracy of text sentiment classification.

Attribute-level sentiment element extraction aims to obtain the word pair < opinion target, opinion word > from texts, which mainly obtain fine-grained evaluation information in the attribute level. Due to the information fragmentation and semantic sparseness of product reviews, it is difficult to capture more comprehensive local information from unstructured texts, which leads to the incorrect extraction of some word pairs. Aimed at the problem, this paper proposes a framework for Attribute-Level Sentiment Element Extraction (ALSEE) towards product reviews. Firstly, a small amount of sample data is selected by random sampling, and multiple features (including part of speech, word distance, dependency relationship and semantic role) which are labelled. The labelled data are used as the training set. Then, the Condition Random Field (CRF) model is applied to extract opinion targets (OT) and opinion words (OW). The self-training strategy is used to achieve the semi-supervised learning of CRF model through iterative training. Finally, target-opinion word pairs with modifying relationship are obtained by dependency parsing. Compared with the existing methods, the proposed framework can effectively extract attribute-level sentiment elements though experimental results.

The existing Emotion-Cause Pair Extraction (ECPE) has made some achievements, and it is applied in many tasks, such as criminal investigations. Previous approaches realised extraction by constructing different networks, but they did not fully exploit the original information of the data, which led to low extraction precision. Moreover, the extraction precision will also be decreased when the model is attacked by adversarial samples. To address the above problems, a new model CL-ECPE is proposed in this article to improve the extraction precision through contrastive learning. First, contrastive sets are constructed by adversarial samples. The contrastive sets are used as the raw data of adversarial training and the test data of the pilot experiment. Then, adversarial training is used to get contrastive features according to the training target. The acquisition of contrastive features can improve extraction precision. Experimental results on the benchmark emotion cause corpus show our method outperforms the state-of-the-art method by over 12.49%, as well as demonstrates the strong robustness of CL-ECPE.

Chinese short comment texts have the characteristics of feature sparseness, interlacing, irregularity, etc., which makes it difficult to fully grasp the overall emotional tendency of users. In response to such problem, the text proposes a new method based on ELECTRA and hybrid neural network. This method can more accurately capture the emotional features of the text, improve the classification effect, enhance the evaluation feedback mechanism, and facilitate user decision-making. First, in the embedding layer, ELECTRA model is used to replace BERT model, which can avoid the inconsistency of the mask training and fine-tuning process of the traditional pre-training model. Then, in the training layer, the self-attention mechanism and the BiLSTM are selected to obtain the fine-grained semantic representation information of the review text more comprehensively. Finally, in the output layer, the softmax classifier classifies the input corpus according to the sentiment characteristics of the Chinese short text. The experimental results show that the proposed model has an efficiently improvement in accuracy and there are some discoveries about the training effect of the pre-training model on text sentiment analysis tasks.

It is challenging to build domain-specific emotional lexicon for film reviews, due to its unique characteristics, such as massive data, endless new login words, and others. To improve the accuracy of film reviews classification, this article proposes a method for extending emotional lexicon based on word distance and point mutual information. First, using the improved K-means++ algorithm to cluster and select seed words with obvious emotional tendencies. Next, the Distance of Word and Point Mutual Information (DW-PMI) algorithm is presented to determine the emotional polarity of emotional words in the domain of film reviews. Four types of vocabulary, including degree adverb, negation, emoticon and emotion dictionary in the film reviews domain are added to the basic emotion dictionary to extend the film reviews emotional lexicon. From the experimental results, the expanded emotional lexicon of the Chinese film reviews can improve the accuracy and preciseness of the film reviews emotion analysis.

Web information contain plentiful, significant knowledge which is eager to be explored by users.Effective semantic layered technology not only can provide theoretical support for knowledge discovery in Web resources, but also can improve the searching efficiency of the related information system.This paper builds the multi-layer theory of association semantic based on the power-law distribution of linking keywords.First, some experiments of four types of keywords with different linking role are done to discover the possible distribution law.Experiment results show that four types of keywords are all reveal power-law distribution.Then, based on the discovered power-law distribution, the multi-layer theory of association semantic is built.The multi-layer theory of association semantic can provide a theoretical support for knowledge recommendation with different particle size on Association Link Network (ALN).