Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword cooccurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work.

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Building a recipe knowledge graph from the perspective of user knowledge demands can provide users with accurate and efficient retrieval results and recipes. Relatively a few ontology studies have been conducted on Chinese recipes. In addition, some existing recipe ontology models are relatively rough, with few dimensions, and others have too many dimensions, that is, they lack versatility and cannot achieve efficient recipe recommendation and query functions. Therefore, this article proposes a general recipe ontology based on user knowledge demands analysed from multi-source recipes. Then, this article selects recipes for common edible flowers with health benefits, an ontology model and constructs a knowledge graph of flower recipes via knowledge extraction, knowledge fusion and other techniques. Finally, the application of constructed flower recipe knowledge graph is discussed, and the results indicated that it can effectively feedback the query results and satisfy the knowledge demands of users.