The Research Of Sentiment Analysis Based On Deep Network Structure

With the rapid development of Internet information technologies,such as e-commerce,online catering,and social media,more and more users express their views and attitudes on goods,services,problems,and events online.Mining the information of texts,pictures,videos,audios,etc.,generated by users,can impact individual behavior decisions,help enterprises and businesses to improve products and services,and assist the government to analyze and guide public feelings.Sentiment analysis,also known as opinion mining,is a research direction to discover users’ intentions and emotional tendencies.Since the 1990 s,more and more researchers have devoted themselves to this research direction.After two or three decades of development,sentiment analysis has become one of the hot topics in data mining,machine learning,and artificial intelligence.The research content contains different granularities,including document,sentence,and word,and multiple modalities,including pictures,videos,and audios besides texts.This dissertation mainly studies extraction and classification tasks in sentiment analysis,including aspect-based sentiment analysis and multimodal sentiment analysis.Especially,the research tasks include aspect term extraction,aspect term-polarity co-extraction,and multimodal sentiment analysis.The main research work and results are summarized as follows:(1)Dependency tree based recursive neural network for aspect term extraction: Previous studies ignored the fusion of grammatical and word ordering features,which is essential in aspect term extraction.Thus,a kind of recursive neural network Bi DTree,which can extract bidirectional dependency structure feature coupling the topology of dependency tree of text,is designed in the dissertation.The proposed network can be achieved via layer-by-layer recursive modeling on a syntax tree from bottom-up and top-down directions.Using the syntax relationships in the dependency tree,the model can capture the long-range dependencies between words.By deeply mining the forward and reverse dependencies between words and further fusing the extracted dependent grammatical features and word ordering features,the representation of natural language sentences is enhanced,and the effectiveness of aspect term extraction can be improved.(2)Dual cross-shared recurrent neural network for aspect term-polarity co-extraction:Previous research mainly trains the models of aspect term extraction and sentiment classification separately because these two tasks belong to different task types.The two tasks are unified as two sequence labeling problems in the dissertation.Then,a Dual cr Oss-shar Ed recurrent neural network DOER is proposed to achieve aspect term extraction and aspect sentiment classification simultaneously.The core idea is to share the representation across the dual recurrent neural networks used to label aspect terms and polarities,respectively,thus promoting the dual network.Besides,two auxiliary training tasks are designed to facilitate feature extraction.One is to predict the aspect term length,which can alleviate the complex dependency problem caused by long aspect terms.The other is sentiment lexicon enhancement,promoting polarity labeling by classifying whether the word is a sentiment word.(3)Gradient harmonized and cascaded labeling for aspect term-polarity co-extraction: To further consider the relationship between aspect terms in polarity labeling,the imbalance of labels,and the effectiveness of the pre-training model in aspect term-polarity co-extraction task,a cascaded labeling-based deep learning model GRACE is proposed in the dissertation.Besides,the gradient harmonized cross-entropy is introduced to train the model.The main idea of the network is to take the generated aspect term label sequence as input and learn the interaction between aspect terms through the self-attention mechanism of Transformer when generating polarity labeling sequence,thus getting better polarity labeling result.The gradient harmonized cross-entropy can effectively alleviate the problem of label imbalance.In addition,the model is also trained with virtual adversarial training to improve the robustness and accuracy.(4)Multi-scale fusion of locally descriptors for multimodal sentiment analysis: To address the shortcomings of the single representation of text modality,the video and speech signals with text modality are integrated to capture more sentiment features and improve the performance of sentiment analysis in the dissertation.A multi-scale feature fusion method Scale VLAD is proposed based on the vector of locally aggregated descriptors,which fuses features of different granularities by aligning the representations of different modalities into a shared vector space.The spatial alignment can effectively alleviate the unclear semantic boundaries of different modalities.A self-supervised shifted clustering loss is also proposed to aggregate the fused features under different clusters as much as possible at each iteration to learn more distinctive features,thus improving classification and regression performance.