Text Sentiment Analysis Based on a New Hybrid Network Model

Abstract

The research of text sentiment analysis based on deep learning is increasingly rich, but the current models still have different degrees of deviation in understanding of semantic information. In order to reduce the loss of semantic information and improve the prediction accuracy as much as possible, the paper creatively combines the doc2vec model with the deep learning model and attention mechanism and proposes a new hybrid sentiment analysis model based on the doc2vec + CNN + BiLSTM + Attention. The new hybrid model effectively exploits the structural features of each part. In the model, the understanding of the overall semantic information of the sentence is enhanced through the paragraph vector pretrained by the doc2vec structure which can effectively reduce the loss of semantic information. The local features of the text are extracted through the CNN structure. The context information interaction is completed through the bidirectional cycle structure of the BiLSTM. The performance is improved by allocating weight and resources to the text information of different importance through the attention mechanism. The new model was built based on Keras framework, and performance comparison experiments and analysis were performed on the IMDB dataset and the DailyDialog dataset. The results have shown that the accuracy of the new model on the two datasets is 91.3% and 93.3%, respectively, and the loss rate is 22.1% and 19.9%, respectively. The accuracy on the IMDB datasets is 1.0% and 0.5% higher than that of the CNN-BiLSTM-Attention model and ATT-MCNN-BGRUM model in the references. Comprehensive comparison has shown the overall performance is improved, and the new model is effective.

Enhancing Text Sentiment Classification with Hybrid CNN-BiLSTM Model on WhatsApp Group

“…Dates, timestamps, cellphone numbers, names of WAG members, emojis (emoticons), and <Media omitted>, which reflects the traces of WAG members sending messages in the form of photographs or multimedia, are all included in the data. The message data doesn't have sentiment classification, so an emotion-based sentiment analysis method is used to determine the classification, using the SentiWordNet emotional lexicon [21,22]. The following processes are used in the labeling sentiment process to build models and analyze emotional words and sentences:…”

Sentiment Labeling and Text Classification Machine Learning for Whatsapp Group

“…This data consists of dates, times, mobile numbers or WAG member names, and messages that include words, sentences, links, emojis (emoticons), and <Media omitted>, which are traces of WAG members sending messages in the form of images or multimedia. Message data had no sentiment classification; therefore, emotional sentiment analysis was based on SentiWordNet emotional dictionary [25] [26] was used to determine the classification. Building models and analyzing emotional words and sentences were performed to label sentiment stages, as shown in figure 2.…”

Social network textual data classification through a hybrid word embedding approach and Bayesian conditional-based multiple classifiers

“…domain and multi domains(Ghorbanali & Sohrabi, 2023b). In the study(Zhou et al, 2022), a hybrid model was presented using doc2vec, CNN, and BiLSTM along with the attention mechanism for the SA of texts.The proposed model signi cantly diminished the loss of semantic information, improved accuracy, and reduced losses from 22.1% and 19.9%. Zhao et al(Zhao et al, 2021), used collected customer comments from e-commerce sites for SA.…”