Abstract
The goal of research on the topics such as sentiment analysis and cognition is to analyze the opinions, emotions, evaluations and attitudes that people hold about the entities and their attributes from the text. The word level affective cognition becomes an important topic in sentiment analysis. Extracting the (attribute, opinion word) binary relationship by word segmentation and dependency parsing, and labeling those by existing emotional dictionary combined with webpage information and manual annotation, this paper constitutes a binary relationship knowledge base. By using knowledge embedding method, embedding each element in (attribute, opinion, opinion word) as a word vector into the Knowledge Graph by TransG, and defining an algorithm to distinguish the opinion between the attribute word vector and the opinion word vector. Compared with traditional method, this engine has the advantages of high processing speed and low occupancy, which makes up the time-costing and high calculating complexity in the former methods.
Highlights
Affective cognition, known as sentiment analysis or opinion mining, aims to analyze the content of people’s emotions, opinions, evaluations and attitudes expressed by entities and their attributes
3 An improve method for Web text affective cognition computing In order to simplify the model, this paper introduces the Translation method in the knowledge graph into the word-level sentiment analysis, which greatly simplifies the model and various parameters required for training by classifying the relationship between the words while vectorising the words
After the word segmentation and labeling method introduced in the previous chapter, we obtained 14,115 triples stored in as data set
Summary
Known as sentiment analysis or opinion mining, aims to analyze the content of people’s emotions, opinions, evaluations and attitudes expressed by entities and their attributes. The entities involved are very extensive and can be products, services, institutions, individuals, events, problems, topics, and so on. Because viewpoint information is very important to people’s actions and behaviors: whether they are individuals or collectives, they often seek opinions and suggestions from others when making decisions. The analysis of viewpoint information has a very wide practical significance. The number of evaluations for a product is often very large, and the number of words is too long. It is almost impossible for an individual or a business to completely read it carefully.
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