Sentiment Classification Of Reviews Research Articles

Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons

In this paper, two techniques for sentiment classification are proposed: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA) for sentiment classification of Gujarati text film reviews. Five different datasets were produced to validate the machine learning-based and lexicon-based methods’ accuracy. The lexicon-based approach employs a sentiment lexicon known as GujSentiWordNet, which identifies sentiments with a sentiment score for feature generation, while in the machine learning-based approach, five classifiers are used: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB) with TF-IDF, and count vectorizer for feature selection. Experiments were carried out and the results obtained were compared using accuracy, precision, recall, and F-score as performance evaluation criteria. According to the test results, the machine learning-based technique improved accuracy by 3 to 10% on average when compared to the lexicon-based approach.

A Manifold-Level Hybrid Deep Learning Approach for Sentiment Classification Using an Autoregressive Model

With the recent expansion of social media in the form of social networks, online portals, and microblogs, users have generated a vast number of opinions, reviews, ratings, and feedback. Businesses, governments, and individuals benefit greatly from this information. While this information is intended to be informative, a large portion of it necessitates the use of text mining and sentiment analysis models. It is a matter of concern that reviews on social media lack text context semantics. A model for sentiment classification for customer reviews based on manifold dimensions and manifold modeling is presented to fully exploit the sentiment data provided in reviews and handle the issue of the absence of text context semantics. This paper uses a deep learning framework to model review texts using two dimensions of language texts and ideogrammatic icons and three levels of documents, sentences, and words for a text context semantic analysis review that enhances the precision of the sentiment categorization process. Observations from the experiments show that the proposed model outperforms the current sentiment categorization techniques by more than 8.86%, with an average accuracy rate of 97.30%.

Text sentiment classification of Amazon reviews using word embeddings and convolutional neural networks

Text sentiment classification of Amazon reviews using word embeddings and convolutional neural networks

Weak tagging and imbalanced networks for online review sentiment classification

Sentiment classification aims to complete the automatic judgment task of text sentiment tendency. In the sentiment classification task of online reviews, traditional deep learning models require a large number of manually annotated samples of sentiment tendency for supervised training. Faced with massive online review data, the feasibility of manual tagging is worrisome. In addition, the traditional deep learning model ignores the imbalanced distribution of the number of classification samples, which will lead to a decline in classification performance in the practical application of the model. Considering that the online review data contains weak tagging information such as scores and labels, and the distribution is imbalanced, a weak tagging and imbalanced networks for online review sentiment classification is constructed. The experimental results show that the model significantly outperforms the traditional deep learning model in the sentiment classification task of hotel review data.

Embedding generation for text classification of Brazilian Portuguese user reviews: from bag-of-words to transformers

Text classification is a natural language processing (NLP) task relevant to many commercial applications, like e-commerce and customer service. Naturally, classifying such excerpts accurately often represents a challenge, due to intrinsic language aspects, like irony and nuance. To accomplish this task, one must provide a robust numerical representation for documents, a process known as embedding. Embedding represents a key NLP field nowadays, having faced a significant advance in the last decade, especially after the introduction of the word-to-vector concept and the popularization of Deep Learning models for solving NLP tasks, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based Language Models (TLMs). Despite the impressive achievements in this field, the literature coverage regarding generating embeddings for Brazilian Portuguese texts is scarce, especially when considering commercial user reviews. Therefore, this work aims to provide a comprehensive experimental study of embedding approaches targeting a binary sentiment classification of user reviews in Brazilian Portuguese. This study includes from classical (Bag-of-Words) to state-of-the-art (Transformer-based) NLP models. The methods are evaluated with five open-source databases with pre-defined data partitions made available in an open digital repository to encourage reproducibility. The Fine-tuned TLMs achieved the best results for all cases, being followed by the Feature-based TLM, LSTM, and CNN, with alternate ranks, depending on the database under analysis.

Automating tourism online reviews: a neural network based aspect-oriented sentiment classification

自动化旅游在线评论：基于神经网络的面向方面的情感分类研究目的本文旨在从方面级对在线旅游-酒店评论的情感进行分类。提出了一种基于神经网络模型的面向方面的情感分类新方法。研究设计/方法/途径本研究使用集成的四层神经网络构建面向方面的情感分类模型：BERT 词向量模型、LSTM、IAOA 机制和线性输出层。该模型在一个开放的训练数据集和从东京餐厅推断的 92,905 条评论上进行了训练、测试和验证。研究发现与其他神经网络相比, 该模型实现了显着更好的性能。研究结果提供了经验证据, 以验证这种新方法在旅游酒店领域的适用性。研究原创性该研究提供了有关神经网络如何提高旅游酒店在线评论的面向方面的情感分类性能的新文献。研究研究局限应该识别更多的情感从而来更加细化衡量旅游酒店体验, 并推荐新的方面/维度可以被自动添加到方面集中, 为新的用餐体验提供动态支持。

RSCOEWR: Radical-Based Sentiment Classification of Online Education Website Reviews

Abstract Online education is becoming more and more popular with the development of the Internet. In particular, due to the COVID-19 pandemic, many countries around the world are increasing the popularity of online education, which makes the research on sentiment classification of course reviews of online education websites an important research direction in natural language processing tasks. Traditional sentiment classification models are mostly based on English. Unlike English, Chinese characters are based on pictograms. Radicals of Chinese characters can also express certain semantics, and characters with the same radical often have similar meanings. Therefore, RSCOEWR, a word-level and radical-level based sentiment classification model for course reviews of Chinese online education websites is proposed, which solves the problem of data sparsity of reviews by feature extraction of multiple dimensions. In addition, a deep learning model based on CNN, BILSTM, BIGRU and Attention is constructed to solve the problem of high dimension and assigning the same attention to context of traditional sentiment classification model. Extensive comparative experiment results show that RSCOEWR outperforms the state-of-the-art sentiment classification models, and the experimental results on public Chinese sentiment classification datasets prove the generalization ability of RSCOEWR.

Sentiment Classification of Educational Tourism Reviews Based on Parallel CNN and LSTM with Attention Mechanism

With the rapid development of the Internet and tourism, the Internet has been widely used in the tourism industry. Tourism enterprises and tourists use the Internet to publish and obtain travel-related information. Educational tourism is a new type of tourism activity. As a combination of “tourism + education,” it has gradually attracted the attention of tourists. With its convenience, fast speed, and low barrier, tourism text data provide great convenience for tourists’ sentiment calculation and have become one of the main sources of big data for tourism. However, the reviews of educational tourism have a lot of redundant information and complex sentence patterns, leading to a relatively low classification accuracy of the existing sentiment analysis algorithms. In order to effectively obtain the implicit semantic information of short text reviews for sentiment orientation recognition, a sentiment classification model for educational tourism online reviews based on parallel CNN and LSTM with multichannel attention mechanism is proposed. Firstly, Word2Vec technique is used, and based on noise word filtering, the feature words of educational tourism reviews are extracted to preprocess the input data set. Then, parallel CNN and LSTM are used to extract text local information and contextual features, and a multichannel attention mechanism is used to extract the attention values from the LSTM output. Finally, the output information of the multichannel attention mechanism is fused to effectively extract text features and focus on important words. The experimental results show that compared with other advanced methods, the proposed algorithm achieves improvements in terms of precision, recall, and F1 value and improves the AUC performance. It will help the educational tourism bases to carry out targeted development and construction in response to tourists feedback, enhance the sense of gain and happiness of tourists in educational tourism activities, improve tourism experience, and promote the rapid development of high-quality educational tourism.

Sentiment Classification: Review of Text Vectorization Methods: Bag of Words, Tf-Idf, Word2vec and Doc2vec

In Sentiment Analysis, there are three (3) approaches namely, machine learning, lexicon-based and ruled based approaches. This study investigates on machine learning approaches which involves text vectorization or word embedding- an essential step in natural language processing tasks since most machine learning algorithms work with numerical input. Text vectorization involves the representation or mapping of words or documents of a corpus to numerical vectors of numbers or real numbers. There are several approaches in the literatures on document/text representation, however this study will focus on three (3) commonly used ones viz; Bag of words, TF-IDF, word2vec and doc2vec, and try to identify the reason behind that for review and recommendation to the researchers in hurry. Review of this study shows that TF-IDF feature vector representations generally outperforms other two (2) vectorization methods word2vec and doc2vec, specifically in book review sentiment classification. And therefore recommended for future studies in book review data set

A metrics-based meta-learning model with meta-pretraining for industrial knowledge graph construction

Isolated data silos and domain-specific knowledge pose challenges for knowledge graph construction in the manufacturing industry, where heterogeneous storage leads to distributed databases with complex schemas. In this article, a resource-based industrial knowledge graph is developed using a few-shot classification algorithm to save on labor and other related costs in industrial knowledge graph construction, and an attribute-based fusion strategy for data fusion and alignment is designed. We also propose a novel metrics-based meta-learning model with meta-pretraining (MMM) to address the few-shot text classification problem. Experiment results show that MMM achieves 87.13% accuracy on the 5-shot text classification benchmark Amazon Review Sentiment Classification (ARSC), outperforming other baselines, such as Induction Networks (85.63%) and Distributional Signatures (81.16%). The MMM achieves a 34.6% accuracy improvement compared with Distributional Signatures (84.34% vs. 62.66%) on 1-shot problems of ARSC, hence highlighting the applicability of our model in low-resource conditions. Based on the proposed methods, we further develop an industrial knowledge graph platform with industrial applications, such as value chain analysis and collaboration, to improve knowledge reuse and service innovation.

Sentiment Classification of Tourism Reviews Based on Visual and Textual Multifeature Fusion

Most of the traditional tourism review sentiment analysis methods ignore the complementary role between review text and images and do not pay attention to the differences in the sentimental expression of different users and the characteristics of the text content of tourism reviews other than the plain text such as emojis. For this reason, a sentiment analysis method for tourism online reviews based on multifeature fusion of images and text is proposed. Firstly, a text sentiment classification model is constructed, and a variety of sentiment features are combined to form a multi-input matrix, and then, it is input into a multichannel CNN (convolutional neural network) to extract sentiment features in order to complete the text sentiment classification. In addition, an image sentiment classification model is constructed by merging the global image and the face image. On the basis of the CNN, the supervision module with weighted loss is added to extract the facial sentiment features, and the facial target sentiment is fused with the sentiment directly recognized by the whole image, and the sentiment polarity of the image in the posted tourism review is determined. Finally, a decision fusion method is designed to fuse the output of the text and image sentiment classification models. The experimental results show that the proposed image-text fusion sentiment classification model effectively enhances the ability of the model to capture sentimental semantics of tourism reviews through the combination of text content features and image sentiment features and achieves excellent results in multiple performance metrics, with better sentiment classification performance than other state-of-the-art models.

Sentiment classification of movie reviews using GA and NeuroGA

Sentiment classification of movie reviews using GA and NeuroGA

A Deep Learning Approach for Sentiment Analysis of COVID-19 Reviews

User-generated multi-media content, such as images, text, videos, and speech, has recently become more popular on social media sites as a means for people to share their ideas and opinions. One of the most popular social media sites for providing public sentiment towards events that occurred during the COVID-19 period is Twitter. This is because Twitter posts are short and constantly being generated. This paper presents a deep learning approach for sentiment analysis of Twitter data related to COVID-19 reviews. The proposed algorithm is based on an LSTM-RNN-based network and enhanced featured weighting by attention layers. This algorithm uses an enhanced feature transformation framework via the attention mechanism. A total of four class labels (sad, joy, fear, and anger) from publicly available Twitter data posted in the Kaggle database were used in this study. Based on the use of attention layers with the existing LSTM-RNN approach, the proposed deep learning approach significantly improved the performance metrics, with an increase of 20% in accuracy and 10% to 12% in precision but only 12–13% in recall as compared with the current approaches. Out of a total of 179,108 COVID-19-related tweets, tweets with positive, neutral, and negative sentiments were found to account for 45%, 30%, and 25%, respectively. This shows that the proposed deep learning approach is efficient and practical and can be easily implemented for sentiment classification of COVID-19 reviews.

A Meta-heuristic Algorithm for the Minimal High-Quality Feature Extraction of Online Reviews

Feature extraction and selection are critical in sentiment analysis (SA) to extract and select only the appropriate features by removing those deemed redundant. As such, the successful implementation of this process leads to better classification accuracy. Inevitably, selecting high-quality minimal features can be challenging given the inherent complication in dealing with over-fitting issues. Most of the current studies used a heuristic method to perform the classification process that will result in selecting and examining only a single feature subset, while ignoring the other subsets that might give better results. This study explored the effect of using the meta-heuristic method together with the ensemble classification method in the sentiment classification of online reviews. Adding to that point, the extraction and selection of relevant features used feature ranking, hyper-parameter optimization, crossover, and mutation, while the classification process utilized the ensemble classifier. The proposed method was tested on the polarity movie review dataset v2.0 and product review dataset (books, electronics, kitchen, and music). The test results indicated that the proposed method significantly improved the classification results by 94%, which far exceeded the existing method. Therefore, the proposed feature extraction and selection method can help in improving the performance of SA in online reviews and, at the same time, reduce the number of extracted features.

Perceiving university students' opinions from Google app reviews

AbstractGoogle app market captures the school of thought of users from every corner of the globe via ratings and text reviews, in a multilinguistic arena. The critique's viewpoint regarding an app is proportional to their satisfaction level. The potential information from the reviews cannot be extracted manually, due to its exponential growth. So, sentiment analysis, by machine learning and deep learning algorithms employing NLP, explicitly uncovers and interprets the emotions. This study performs the sentiment classification of the app reviews and identifies the university students' behavior toward the app market via exploratory analysis. We applied machine learning algorithms using the TP, TF, and TF‐IDF text representation scheme and evaluated its performance on Bagging, an ensemble learning method. We used word embedding, GloVe, on the deep learning paradigms. Our model was trained on Google app reviews and tested on students' app reviews (SAR). The various combinations of these algorithms were compared among each other using F‐score and accuracy and inferences were highlighted graphically. SVM, among other classifiers, gave fruitful accuracy (93.41%), F‐score (0.89) on bi‐gram + TF‐IDF scheme. Bagging enhanced the performance of LR and NB with accuracy 87.88% and 86.69% and F‐score 0.86 and 0.78 respectively. Overall, LSTM on Glove embedding recorded the highest accuracy (95.2%) and F‐score (0.88).

A Scheme of Pairwise Feature Combinations to Improve Sentiment Classification Using Book Review Dataset

Sentiment Analysis is a Natural Language Processing (NLP) domain related to the identification or extraction of user sentiments or opinions from written language. Although the approaches to achieve the goals may vary, Machine Learning (ML) methods are gradually becoming the preferred method because of their ability to automatically draw useful insight from data regardless of their complexity. However, an important prerequisite for most ML algorithms to learn from text data is to encode them into numerical vectors. Popular approaches to this include word level representation methods TF-IDF, distributed word representations (word2vec) and distributed document representations (doc2vec). Each of these methods has demonstrated remarkable success in representing the encoded text, however we found that no method has been set to be excellence in all tasks. Motivated by this challenge, an improved scheme of pairwise fusion are proposed for sentiment classification of book reviews. In the experimental findings, Artificial Neural Networks (ANN) and Logistic Regression (LR) classifiers showed that the proposed scheme improved the performance compared to the single method vectorization method. We see that TF-IDF-word2vec performed best among other methods with a mean accuracy of 91.0% (ANN) and 92.5% (LR); showed an improvement of 0.7% and 0.2% respectively over TF-IDF which is the best single vector method. Thus, the proposed method can used as a compact alternative to the popular bag-of-n-gram models as it captures contextual information of encoded document with a less sparse data.

Leveraging Pre-Trained Contextualized Word Embeddings to Enhance Sentiment Classification of Drug Reviews

Traditionally, pharmacovigilance data are collected during clinical trials on a small sample of patients and are therefore insufficient to adequately assess drugs. Nowadays, consumers use online drug forums to share their opinions and experiences about medication. These feedbacks, which are widely available on the web, are automatically analyzed to extract relevant information for decision-making. Currently, sentiment analysis methods are being put forward to leverage consumers' opinions and produce useful drug monitoring indicators. However, these methods' effectiveness depends on the quality of word representation, which presents a real challenge because the information contained in user reviews is noisy and very subjective. Over time, several sentiment classification problems use machine learning methods based on the traditional bag of words model, sometimes enhanced with lexical resources. In recent years, word embedding models have significantly improved classification performance due to their ability to capture words' syntactic and semantic properties. Unfortunately, these latter models are weak in sentiment classification tasks because they are unable to encode sentiment information in the word representation. Indeed, two words with opposite polarities can have close word embeddings as they appear together in the same context. To overcome this drawback, some studies have proposed refining pre-trained word embeddings with lexical resources or learning word embeddings using training data. However, these models depend on external resources and are complex to implement. This work proposes a deep contextual word embeddings model called ELMo that inherently captures the sentiment information by providing separate vectors for words with opposite polarities. Different variants of our proposed model are compared with a benchmark of pre-trained word embeddings models using SVM classifier trained on Drug Review Dataset. Experimental results show that ELMo embeddings improve classification performance in sentiment analysis tasks on the pharmaceutical domain.

Sentiment Analysis in Myanmar Language using Convolutional LSTM Neural Network

In recent years, there has been an increasing use of social media among people in Myanmar and writing review on social media pages about the product, movie, and trip are also popular among people. Moreover, most of the people are going to find the review pages about the product they want to buy before deciding whether they should buy it or not. Extracting and receiving useful reviews over interesting products is very important and time consuming for people. Sentiment analysis is one of the important processes for extracting useful reviews of the products. In this paper, the Convolutional LSTM neural network architecture is proposed to analyse the sentiment classification of cosmetic reviews written in Myanmar Language. The paper also intends to build the cosmetic reviews dataset for deep learning and sentiment lexicon in Myanmar Language.

Improving Sentiment Classification of Restaurant Reviews with Attention-Based Bi-GRU Neural Network

In the era of Web 2.0, there is a huge amount of user-generated content, but the huge amount of unstructured data makes it difficult for merchants to provide personalized services and for users to extract information efficiently, so it is necessary to perform sentiment analysis for restaurant reviews. The significant advantage of Bi-GRU is the guaranteed symmetry of the hidden layer weight update, to take into account the context in online restaurant reviews and to obtain better results with fewer parameters, so we combined Word2vec, Bi-GRU, and Attention method to build a sentiment analysis model for online restaurant reviews. Restaurant reviews from Dianping.com were used to train and validate the model. With F1-score greater than 89%, we can conclude that the comprehensive performance of the Word2vec+Bi-GRU+Attention sentiment analysis model is better than the commonly used sentiment analysis models. We applied deep learning methods to review sentiment analysis in online food ordering platforms to improve the performance of sentiment analysis in the restaurant review domain.

Sentiment classification of user's reviews on drugs based on global vectors for word representation and bidirectional long short-term memory recurrent neural network

<p>The process of product development in the health sector, especially pharmaceuticals, goes through a series of precise procedures due to its directrelevance to human life. The opinion of patients or users of a particular drugcan be relied upon in this development process, as the patients convey their experience with the drugs through their opinion. The social media field provides many datasets related to drugs through knowing the user's ratingand opinion on a drug after using it. In this work, a dataset is used that includes the user’s rating and review on the drug, for the purpose of classifying the user’s opinions (reviews) whether they are positive ornegative. The proposed method in this article includes two phases. The first phase is to use the Global vectors for word representation model for converting texts into the vector of embedded words. As for the second stage, the deep neural network (Bidirectional longshort-termmemory) is employedin the classification of reviews. The user's rating is used as a ground truth inevaluating the classification results. The proposed method present sencouraging results, as the classification results are evaluated through threecriteria, namely Precision, Recall and F-score, whose obtained values equal(0.9543, 0.9597and0.9558), respectively. The classification results of theproposed method are compared to a number of classifiers, and it was noticed that the results of the proposed method exceed those of the alternative classifiers.</p>