RETRACTED: Multi-neural network-based sentiment analysis of food reviews based on character and word embeddings

Abstract

Sentiment analysis becomes one of the most active research hotspots in the field of natural language processing tasks in recent years. However, the inability to fully and effectively use emotional information is a problem in present deep learning models. A single Chinese character has different meanings in different words, and the character embeddings are combined with the word embeddings to extract more precise meaning information. In this paper, a single Chinese character and word are used as input units to train. Based on BLSTM, the attention mechanism based on vocabulary semantics in food field is introduced to realize distance-related sequence semantic feature extraction. CNN is used to realize semantic sentiment classification of sequence semantic features. Therefore, a model based on multi-neural network for sentiment information extraction and analysis is proposed. Experiments show that the model has excellent characteristics in sentiment analysis and obtains high accuracy and F value.

Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures

“…In order to conduct a comparative experiment, using the same data, the method proposed in Long Jiang's model was implemented [30]. Table 5 shows the accuracy and recall rates of Long Jiang's model and the model proposed in this study.…”

A Review and Comparative Analysis of Sentiment Analysis Techniques

“…SentiStrength has been applied to classify the sentiment of textual messages. This algorithm is based on a lexicon that contains words and sentences that are used mostly in social networking [31]. Additionally, the lexicon is built by integrating three online dictionaries, saving only the occurrence words that are repeated.…”

“…Such contents are generally brief and lack explicit sentiment words. To analyze the context of messages and understand the sentiment associated with them [31]. a model has been proposed that entails the semantic correlation between various modalities as well as the impacts of tweet context information.…”

“…The deep learning model mentioned above can be considered a means to solving sentiment analysis problems. For example, in a recent study of Chinese language [31], the failure to fully utilize emotional information was identified as an issue. A single Chinese character might have multiple meanings in different words, and the character embeddings are combined with the word embeddings to obtain a more accurate meaning of the information.…”