TextConvoNet: a convolutional neural network based architecture for text classification

Prediction of molecular-specific mutagenic alerts and related mechanisms of chemicals by a convolutional neural network (CNN) model based on SMILES split

Structural alerts (SAs) are essential to identify chemicals for toxicity evaluation and health risk assessment. We constructed a novel SMILES split-based deep learning model (SSDL) that was trained and verified with 5850 chemicals from the ISSSTY database and 384 external test chemicals from published papers. The training accuracy was above 0.90 and the evaluation metrics (precision, recall and F1-score) all reached 0.78 or above on both internal and external test chemicals. In this model, the molecular-specific fragment importance of chemicals was first quantified independently. Then, the SA identification method based on the importance of these fragments was statistically analyzed and verified with the ISSSTY test and external test chemicals containing one of 28 typical SAs, and most of the performances were better than that of expert rules. Furthermore, a mutagenicity mechanism prediction method was developed using 237 chemicals with four known mutagenic mechanisms based on molecular similarity calibrated by the SSDL method and fragment importance, which significantly improved accuracy in three mechanisms and had comparable accuracy in the other one compared to traditional methods. Overall, the SSDL model quantifying fragment toxicity within molecules would be a novel potentially powerful tool in the determination and visualization of molecular-specific SAs and the prediction of mutagenicity mechanisms for environmental or industrial compounds and drugs.

Deep learning-based application for multilevel sentiment analysis of Indonesian hotel reviews

Purpose

In this study, we present a web-based application that retrieves hotel review documents in Indonesian languages from an online travel agent (OTA) and analyses their sentiments from the coarse-grained document to the fine-grained aspect level.

Design

/Methodology/Approach: There are four main stages in this study: development of sentiment analysis model at the document level based on a convolutional neural network (CNN), development of sentiment analysis model at the aspect level based on an improved long short-term memory (LSTM), model deployment for multilevel sentiment analysis in a web-based application, and its performance evaluation. The developed application uses several sentiment visualizations types at coarse-grained and fine-grained levels, such as pie charts, line charts, and bar charts.

Finding

The application's functionality was demonstrated in practice based on three datasets from three OTA websites, which were analyzed and evaluated based on several matrices, namely, the precision, recall, and F1-score. The results revealed that the performance for the F1-score was 0.95 ± 0.03, 0.87 ± 0.02, and 0.92 ± 0.07 for document-level sentiment analysis, aspect-level sentiment analysis, and aspect-polarity detection, respectively.

Originality

The developed application (Sentilytics 1.0) can analyze sentiment at document and aspect levels. The two levels of sentiment analysis are based on two models generated by fine-tuning CNN and LSTM models using specific architectures and domain data (Indonesian hotel reviews).

Imbalanced Text Sentiment Classification Based on Multi-Channel BLTCN-BLSTM Self-Attention

With the continuous expansion of the field of natural language processing, researchers have found that there is a phenomenon of imbalanced data distribution in some practical problems, and the excellent performance of most methods is based on the assumption that the samples in the dataset are data balanced. Therefore, the imbalanced data classification problem has gradually become a problem that needs to be studied. Aiming at the sentiment information mining of an imbalanced short text review dataset, this paper proposed a fusion multi-channel BLTCN-BLSTM self-attention sentiment classification method. By building a multi-channel BLTCN-BLSTM self-attention network model, the sample after word embedding processing is used as the input of the multi-channel, and after fully extracting features, the self-attention mechanism is fused to strengthen the sentiment to further fully extract text features. At the same time, focus loss rebalancing and classifier enhancement are combined to realize text sentiment predictions. The experimental results show that the optimal F1 value is up to 0.893 on the Chnsenticorp-HPL-10,000 corpus. The comparison and ablation of experimental results, including accuracy, recall, and F1-measure, show that the proposed model can fully integrate the weight of emotional feature words. It effectively improves the sentiment classification performance of imbalanced short-text review data.