Design of adaptive ensemble classifier for online sentiment analysis and opinion mining

Scenario-feature identification from online reviews based on BERT

Scenario endows a product with meanings. It has become the key to win the competition to design a product according to specific usage scene. Traditional scenario identification and product feature association methods have disadvantages such as subjectivity, high cost, coarse granularity, and limited scenario can be identified. In this regard, we propose a BERT-based scenario-feature identification model to effectively extract the information about users' experience and usage scene from online reviews. First, the scenario-feature identification framework is proposed to depict the whole identification process. Then, the BERT-based scene-sentence recognition model is constructed. The Skip-gram and word vector similarity methods are used to construct the scene and feature lexicon. Finally, the triad is constructed through the analysis of scene-feature co-occurrence matrix, which realizes the association of scenario and product features. This proposed model is of great practical value for product developers to better understand customer's requirements in specific scenarios. The experiments of scenario-feature identification from the reviews of Pacific Auto verifies the effectiveness of this method.

Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble

The exponential rise in social media via microblogging sites like Twitter has sparked curiosity in sentiment analysis that exploits user feedback towards a targeted product or service. Considering its significance in business intelligence and decision-making, numerous efforts have been made in this area. However, lack of dictionaries, unannotated data, large-scale unstructured data, and low accuracies have plagued these approaches. Also, sentiment classification through classifier ensemble has been underexplored in literature. In this article, we propose a Semantic Relational Machine Learning (SRML) model that automatically classifies the sentiment of tweets by using classifier ensemble and optimal features. The model employs the Cascaded Feature Selection (CFS) strategy, a novel statistical assessment approach based on Wilcoxon rank sum test, univariate logistic regression assisted significant predictor test and cross-correlation test. It further uses the efficacy of word2vec-based continuous bag-of-words and n-gram feature extraction in conjunction with SentiWordNet for finding optimal features for classification. We experiment on six public Twitter sentiment datasets, the STS-Gold dataset, the Obama-McCain Debate (OMD) dataset, the healthcare reform (HCR) dataset and the SemEval2017 Task 4A, 4B and 4C on a heterogeneous classifier ensemble comprising fourteen individual classifiers from different paradigms. Results from the experimental study indicate that CFS supports in attaining a higher classification accuracy with up to 50% lesser features compared to count vectorizer approach. In Intra-model performance assessment, the Artificial Neural Network-Gradient Descent (ANN-GD) classifier performs comparatively better than other individual classifiers, but the Best Trained Ensemble (BTE) strategy outperforms on all metrics. In inter-model performance assessment with existing state-of-the-art systems, the proposed model achieved higher accuracy and outperforms more accomplished models employing quantum-inspired sentiment representation (QSR), transformer-based methods like BERT, BERTweet, RoBERTa and ensemble techniques. The research thus provides critical insights into implementing similar strategy into building more generic and robust expert system for sentiment analysis that can be leveraged across industries.

Weighted Joint Sentiment-Topic Model for Sentiment Analysis Compared to ALGA: Adaptive Lexicon Learning Using Genetic Algorithm

Latent Dirichlet Allocation (LDA) is an approach to unsupervised learning that aims to investigate the semantics among words in a document as well as the influence of a subject on a word. As an LDA-based model, Joint Sentiment-Topic (JST) examines the impact of topics and emotions on words. The emotion parameter is insufficient, and additional parameters may play valuable roles in achieving better performance. In this study, two new topic models, Weighted Joint Sentiment-Topic (WJST) and Weighted Joint Sentiment-Topic 1 (WJST1), have been presented to extend and improve JST through two new parameters that can generate a sentiment dictionary. In the proposed methods, each word in a document affects its neighbors, and different words in the document may be affected simultaneously by several neighbor words. Therefore, proposed models consider the effect of words on each other, which, from our view, is an important factor and can increase the performance of baseline methods. Regarding evaluation results, the new parameters have an immense effect on model accuracy. While not requiring labeled data, the proposed methods are more accurate than discriminative models such as SVM and logistic regression in accordance with evaluation results. The proposed methods are simple with a low number of parameters. While providing a broad perception of connections between different words in documents of a single collection (single-domain) or multiple collections (multidomain), the proposed methods have prepared solutions for two different situations (single-domain and multidomain). WJST is suitable for multidomain datasets, and WJST1 is a version of WJST which is suitable for single-domain datasets. While being able to detect emotion at the level of the document, the proposed models improve the evaluation outcomes of the baseline approaches. Thirteen datasets with different sizes have been used in implementations. In this study, perplexity, opinion mining at the level of the document, and topic_coherency are employed for assessment. Also, a statistical test called Friedman test is used to check whether the results of the proposed models are statistically different from the results of other algorithms. As can be seen from results, the accuracy of proposed methods is above 80% for most of the datasets. WJST1 achieves the highest accuracy on Movie dataset with 97 percent, and WJST achieves the highest accuracy on Electronic dataset with 86 percent. The proposed models obtain better results compared to Adaptive Lexicon learning using Genetic Algorithm (ALGA), which employs an evolutionary approach to make an emotion dictionary. Results show that the proposed methods perform better with different topic number settings, especially for WJST1 with 97% accuracy at |Z| = 5 on the Movie dataset.