Sentiment Classification Method Research Articles

Triplet Contrastive Learning for Aspect Level Sentiment Classification

The domain of Aspect Level Sentiment Classification, in which the sentiment toward a given aspect is analyzed, attracts much attention in NLP. Recently, the state-of-the-art Aspect Level Sentiment Classification methods are devised by using the Graph Convolutional Networks to deal with both the semantics and the syntax of the sentence. Generally, the parsing of syntactic structure inevitably incorporates irrelevant information toward the aspect. Besides, the syntactic and semantic alignment and uniformity that contribute to the sentiment delivery is currently neglected during processing. In this work, a Triplet Contrastive Learning Network is developed to coordinate the syntactic information and the semantic information. To start with, the aspect-oriented sub-tree is constructed to replace the syntactic adjacency matrix. Further, a sentence-level contrastive learning scheme is proposed to highlight the features of sentiment words. Based on The Triple Contrastive Learning, the syntactic information and the semantic information are thoroughly interacted and coordinated whilst the global semantics and syntax can be exploited. Extensive experiments are performed on three benchmark datasets and achieve accuracies (BERT-based) of 87.40, 82.80, 77.55 on Rest14, Lap14, and Twitter datasets, which demonstrate that our approach achieves state-of-the-art results in Aspect Level Sentiment Classification task.

Impact of demography on linguistic aspects and readability of reviews and performances of sentiment classifiers

This study investigates how user demographics influence the linguistic characteristics and readability of the reviews and the performance of sentiment classification methods. Two restaurant review datasets representing consumers of distinct demographics, such as English language fluency levels, socio-culture, and geographic locations, are compared. It is observed that demographic dissimilarities affect various linguistic attributes, such as sentence structure and vocabulary usage of reviews. The Mann–Whitney U test indicates significant differences in these two sets of reviews on various linguistic aspects. Regarding the readability of the two corpora, no noticeable difference is perceived by Flesch Reading Ease (FRE) test. The discrepancy in the results of lexicon-based and machine learning-based sentiment classification methods in two corpora suggests that demography has an influential role in the performance of sentiment classifiers.

Sentiment Analysis on Customer Perception towards Products and Services of Restaurant in Labuan Bajo

Analysis of consumer sentiment towards restaurant products and services in the form of reviews on various digital platforms such as Tripadvisor determines business sustainability and the image of tourist destinations. This study aims to classify visitor sentiments as Tripadvisor users towards Happy Banana Komodo, MadeInItaly, Mediterraneo, and La Cucina restaurants in Labuan Bajo. The research stages are divided into three parts, namely the stages of data collection, data processing, classification and evaluation of model performance, and interpretation of data and information. At the data collection stage, Tripadvisor user reviews of products and services at each restaurant are mined using the Webharvy application based on the configuration of the customer name, date of examination, rating, review title, and review. The data mining results are cleaned and prepared to be managed using the RapidMiner application at the data processing stage. The classification and evaluation stage of model performance is the implementation and testing of classification algorithms that are relevant to the dataset, namely Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN). The findings of this study indicate the implementation of the positive and negative sentiment classification method for the comprehensive review data from the Tripadvisor website for the products and services of restaurants Happy Banana Komodo, MadeInITaly, Mediterraneo, and La Cucina Restaurants are relevant with k-Nearest Neighbor (k-NN) with accuracy value of 99.27% ​​, a precision value of 100%, and a recall of 98.53%.

Deep-Learning-Based Financial Message Sentiment Classification in Business Management.

A deep-learning-based financial text sentiment classification method is proposed in this paper, which can provide a reference for business management. In the proposed method, domain adaptation is adopted to solve the common problem of insufficient labeled samples in the financial textual domain. Specifically, in the classification process, the seq2seq model is firstly adopted to extract the abstract from the financial message, which can reduce the influence of invalid information and speed up processing. In the process of sentiment classification, a bidirectional LSTM model is adopted for classification, which can more comprehensively make use of context information. Experiments are carried out to testify the proposed method through the open-source data set. It can be seen that the proposed method can effectively transfer from the reduced Amazon data set to the StockTwits financial text data set. Compared with the parameter-frozen-based method and the SDA-based method, the recognition rates have improved by 0.5% and 6.8%, respectively. If the target domain data set can be directly adopted for training, the recognition rate of the proposed method is higher than that of the SVM method and the LSTM method by 8.3% and 4.5%, respectively.

Study on sentiment classification strategies based on the fuzzy logic with crow search algorithm

In recent times, sentiment analysis research has gained wide popularity. That situation causes the importance of online applications that allow users to express their opinions on events, services, or products through social media applications such as Twitter, Facebook, and Amazon. This paper proposes a novel sentiment classification method according to the fuzzy rule-based system (FRBS) with the crow search algorithm (CSA). FRBS is used to classify the polarity of sentences or documents, and the CSA is employed to optimize the best output from the fuzzy logic algorithm. The FRBS is applied to extract the sentiment and classify its polarity into negative, neutral, and positive. Sometimes, the outputs of the FRBS must be enhanced, especially since many variables are present and the rules between them overlap. For such cases, the CSA is used to solve this limitation faced by FRBS to optimize the outputs of FRBS and achieve the best result. This study compares the performance of the proposed model with different machine learning algorithms, such as SVM, maximum entropy, boosting, and SWESA. It tests the model on three famous data sets collected from Amazon, Yelp, and IMDB. Experimental results demonstrate the effectiveness of the proposed model and achieve competitive performance in terms of accuracy, recall, precision, and the F–score.

Target-specific sentiment analysis method combining word-masking data enhancement and adversarial learning

Abstract Target-specific sentiment analysis is an emerging topic in the field of text mining but current approaches to deriving the polarity of a sentence suffer from two main drawbacks: on one hand, we lack of a large and well-curated corpus, and on the other hand, current solutions based on deep learning are particularly vulnerable to the attack of adversarial samples. A novel target-specific sentiment classification method is proposed. Firstly, the method of masking target entities is applied to replace synonyms and insert words randomly; secondly, the target-specific sentiment classification model of adversarial learning is constructed with six baseline models; finally, we combine data enhancement and adversarial learning to construct target-specific sentiment classification model. Experimental results show that Macro-F1 values are improved by 0.30–2.91, 0.88–2.42 and 0.13–1.94% compared to the six baseline models by using Laptop14, Restaurant14 and Twitter original datasets, respectively, using Adversarial learning. Using word-masking data enhancement samples and Adversarial learning from Laptop14, Restaurant14 and Twitter shows that Macro-F1 values are improved by 0.9–2.64, 1.59–3.09 and 0.18–1.71% compared to the six baseline (SC), respectively. Our method can effectively improve the quality of samples, it improves the classification performance and the capability of adversarial samples defense.

Spontaneously generated online patient experience data - how and why is it being used in health research: an umbrella scoping review

PurposeSocial media has led to fundamental changes in the way that people look for and share health related information. There is increasing interest in using this spontaneously generated patient experience data as a data source for health research. The aim was to summarise the state of the art regarding how and why SGOPE data has been used in health research. We determined the sites and platforms used as data sources, the purposes of the studies, the tools and methods being used, and any identified research gaps.MethodsA scoping umbrella review was conducted looking at review papers from 2015 to Jan 2021 that studied the use of SGOPE data for health research. Using keyword searches we identified 1759 papers from which we included 58 relevant studies in our review.ResultsData was used from many individual general or health specific platforms, although Twitter was the most widely used data source. The most frequent purposes were surveillance based, tracking infectious disease, adverse event identification and mental health triaging. Despite the developments in machine learning the reviews included lots of small qualitative studies. Most NLP used supervised methods for sentiment analysis and classification. Very early days, methods need development. Methods not being explained. Disciplinary differences - accuracy tweaks vs application. There is little evidence of any work that either compares the results in both methods on the same data set or brings the ideas together.ConclusionTools, methods, and techniques are still at an early stage of development, but strong consensus exists that this data source will become very important to patient centred health research.

Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data

This paper proposes an effective and optimal sentiment classification method named Penguin Rider optimization algorithm-based Deep Recurrent Neural Network (PeROA-based Deep RNN) to perform sentiment classification using political reviews. However, the proposed PeROA is developed by incorporating the Penguins Search Optimization Algorithm (PeSOA) with the Rider Optimization Algorithm (ROA). The sentiment classification process is progressed using the Deep RNN classifier, which in turn generate the optimal solution based on the fitness measure. Accordingly, the function with the minimal error value is accepted as the best solution. The sentiment-based features enable the classifier to perform better classification result with respect to the sentiment tweets. However, the proposed PeROA-based Deep RNN obtained better performance using the metrics, like accuracy, sensitivity, specificity, recall, F-measure, thread score, NPV, FPR,FNR and FDR with the values of 92.030%, 92.030%, 92.235%, 92.030%, 92.030%, 92.030%, 92.030%, 3.105%, 3.11%, and 3.105%, respectively.

Sentiment Classification Method Based on Blending of Emoticons and Short Texts.

With the development of Internet technology, short texts have gradually become the main medium for people to obtain information and communicate. Short text reduces the threshold of information production and reading by virtue of its short length, which is in line with the trend of fragmented reading in the context of the current fast-paced life. In addition, short texts contain emojis to make the communication immersive. However, short-text content means it contains relatively little information, which is not conducive to the analysis of sentiment characteristics. Therefore, this paper proposes a sentiment classification method based on the blending of emoticons and short-text content. Emoticons and short-text content are transformed into vectors, and the corresponding word vector and emoticon vector are connected into a sentencing matrix in turn. The sentence matrix is input into a convolution neural network classification model for classification. The results indicate that, compared with existing methods, the proposed method improves the accuracy of analysis.

A Sentiment Classification Method of Web Social Media Based on Multidimensional and Multilevel Modeling

Sentiment classification of web social media faces the problem of text context semantics missing. The existing research mainly solves the problem of text context semantic missing by mining language symbol information in web social media text, seldom considering the emoticon symbols and punctuation symbols in web social media text. Similar to language symbols, emoticons’ symbols and punctuation symbols in web social media text also contain certain sentiment information. In order to make full use of sentiment information contained in web social media to solve the problem of text context semantics missing, we propose a sentiment classification method of web social media based on multidimensional and multilevel modeling. By modeling web social media text from three dimensions (language symbols, emoticons’ symbols, and punctuation symbols) and three levels (words, sentences, and documents) based on a deep learning framework, in this article, we attempt to solve text context semantics missing faced by the sentiment classification of web social media and improve the accuracy of sentiment classification of web social media. The experimental results on Sina Weibo and Twitter datasets show that the average accuracy of our method is 0.9479, which achieves more than 5.86% performance compared with the existing sentiment classification methods.

Design of Service Robot Based on User Emotion Recognition and Environmental Monitoring.

Robots may be able to comprehend human emotions better by adding speech emotion recognition and environment monitoring functions to human-computer interaction systems. Robots can offer more humanized services by adapting to human emotions, resulting in a comfortable and cordial interaction between humans and robots. Improve the environment for communication and computer-human interaction and also interactive computer experience. In order for service robots to perform fluid human-computer interaction, this paper designs a sentiment analysis model based on CNN (convective neural network) to detect the feeling of interacting objects. It also builds a sentiment analysis model and an open domain dialogue system suitable for service robots. Examine the emotions experienced by the objects while they conversed. According to test results, the sentiment classification method used in this article performs more accurately on the dataset than the conventional model, and the final sentiment analysis model's F1 value can reach 0.931, which is better able to identify an emotional state. Using all voice samples as the input content of the network would eliminate the confusion between neutral emotions and other nonneutral emotions, boosting the accuracy of sentiment analysis in comparison to the fixed-length processing method of dividing or filling samples.

Text sentiment classification method based on DPCNN and BiLSTM

In recent years, deep learning network models have been widely used in the aspect of text emotion classification and have achieved remarkable achievements. The traditional TextCNN network can only extract local spatial features of sentences, while the improved DPCNN model has the ability to capture long-distance dependence of the text by deepening the network depth. At the same time, bi-LSTM model is characterized by learning temporal information of text. Therefore, this paper combines the two models, which can not only obtain the spatial local information of the text, but also further strengthen the ability to understand and learn the semantic association information of the text. Experimental results show that the classification effect of the model used in this paper is better than the single model.

Cross-Modal Sentiment Sensing with Visual-Augmented Representation and Diverse Decision Fusion.

The rising use of online media has changed the social customs of the public. Users have become accustomed to sharing daily experiences and publishing personal opinions on social networks. Social data carrying emotion and attitude has provided significant decision support for numerous tasks in sentiment analysis. Conventional methods for sentiment classification only concern textual modality and are vulnerable to the multimodal scenario, while common multimodal approaches only focus on the interactive relationship among modalities without considering unique intra-modal information. A hybrid fusion network is proposed in this paper to capture both inter-modal and intra-modal features. Firstly, in the stage of representation fusion, a multi-head visual attention is proposed to extract accurate semantic and sentimental information from textual contents, with the guidance of visual features. Then, multiple base classifiers are trained to learn independent and diverse discriminative information from different modal representations in the stage of decision fusion. The final decision is determined based on fusing the decision supports from base classifiers via a decision fusion method. To improve the generalization of our hybrid fusion network, a similarity loss is employed to inject decision diversity into the whole model. Empiric results on five multimodal datasets have demonstrated that the proposed model achieves higher accuracy and better generalization capacity for multimodal sentiment analysis.

Sentiment classification in social media data by combining triplet belief functions

AbstractSentiment analysis is an emerging technique that caters for semantic orientation and opinion mining. It is increasingly used to analyze online reviews and posts for identifying people's opinions and attitudes to products and events in order to improve business performance of companies and aid to make better organizing strategies of events. This paper presents an innovative approach to combining the outputs of sentiment classifiers under the framework of belief functions. It consists of the formulation of sentiment classifier outputs in the triplet evidence structure and the development of general formulas for combining triplet functions derived from sentiment classification results via three evidential combination rules along with comparative analyses. The empirical studies have been conducted on examining the effectiveness of our method for sentiment classification individually and in combination, and the results demonstrate that the best combined classifiers by our method outperforms the best individual classifiers over five review datasets.

Exploring user historical semantic and sentiment preference for microblog sentiment classification

Microblog text is usually very short, thereby challenging existing sentiment classification methods by providing models with little context. Recently, historical user information has been widely used in many real-world applications, such as recommender systems. However, few research works consider user historical states in the loop of microblog sentiment analysis. In this work, we propose to involve historical user information for microblog sentiment analysis to alleviate the context sparsity problem. In particular, we propose a novel neural microblog sentiment classification method which learns informative representations of microblog posts by exploiting both a user’s contextual information and his/her historical state information. The proposed method consists of four components, i.e., a micropost encoder, a user historical sentiment encoder, a User Historical Semantic Encoder, and a micropost sentiment classification component. Extensive experiments are conducted on real-world data collected from Weibo, and experimental results show that the proposed approach achieves superior performance as compared to state-of-the-art baselines.

Survey Paper on Recommendation System for Tourist Reviews using Aspect Based Sentiment Classification

The tourism and travel sector is trying to provide different facility using a large amount of data collected from different tourism web sites. The tourist easily retrieves to reviews, evidence of different tourists and accesses them properly. It helps tourists have made the planning of visit to tourism place. So that, a major challenge faced by tourism sector is to utilize the accumulate information for detecting tourist preferences. Unfortunately, some user's comments are irrelevant and complex for understanding and long-winded these become hard for recommendation. Aspect based sentiment classification methods have shown promise in overcome the issue. In existing not much work on aspect based sentiment with classification. Here in this paper represents a framework of aspect based sentiment classification recommendation system that will not only identify the aspects very efficiently but can perform classification task with high accuracy using machine learning algorithms. This framework helps tourists to find better tourist spot, hotels, restaurant and resort in a city, and here performance has been evaluated by conducting experiments on Yelp and foursquare real-time datasets.

GL-GCN: Global and Local Dependency Guided Graph Convolutional Networks for aspect-based sentiment classification

Aspect-based sentiment classification, which aims at identifying the sentiment polarity of a sentence towards the specified aspect, has become a crucial task for sentiment analysis. Existing methods have proposed effective models and achieved satisfactory results, but they mainly focus on exploiting local structure information of a given sentence, such as locality, sequentiality or syntactical dependency constraints within the sentence. Recently, some research works, which utilizes global dependency information, has attracted increasing interest and significantly boosts the performance of text classification. In this paper, we simultaneously introduce both global structure information and local structure information into the task of aspect-based sentiment classification, and propose a novel aspect-based sentiment classification approach, i.e., Global and Local Dependency Guided Graph Convolutional Networks (GL-GCN). In particular, we exploit the syntactic dependency structure as well as sentence sequential information (e.g., the output of BiLSTM) to mine the local structure information of a sentence. On the other hand, we construct a word-document graph using the entire corpus to reveal the global dependency information between words. In addition, an attention mechanism is leveraged to effectively fuse both global and local dependency structure signals. Extensive experiments are conducted on five benchmark datasets in terms of both Accuracy and F1-Score, and the results illustrate that our proposed framework outperforms state-of-the-art methods for aspect-based sentiment classification. The model is implemented using PyTorch and is trained on GPU GeForce GTX 2080 Ti.

Sentiment classification using attention mechanism and bidirectional long short-term memory network

We propose a sentiment classification method for large scale microblog text based on the attention mechanism and the bidirectional long short-term memory network (SC-ABiLSTM). We use an experimental study to compare our proposed method with baseline methods using real world large-scale microblog data. Comparing the accuracy of the baseline methods to the accuracy of our model, we demonstrate the efficacy of our proposed method. While sentiment classification of social media data has been extensively studied, the main novelty of our study is the implementation of the attention mechanism in a deep learning network for analyzing large scale social media data.

Analisis Sentimen Movie Review menggunakan Word2Vec dan metode LSTM Deep Learning

The increasing number of internet users is directly in line with the increasing number of data on the internet that is available for analysis, especially data in text form. The availability of this text data encourages a lot of sentiment analysis research. However, it turns out that the availability of abundant text data is also one of the challenges in sentiment analysis research. Datasets that consist of long and complex text documents require a different approach. In this study, LSTM was chosen to be used as a sentiment classification method. This research uses a movie review dataset that consists of 25,000 review documents, with an average length per review is 233 words. The research uses CBOW and Skip-Gram methods on word2vec to form a vector representation of each word (word vector) in the corpus data. Several dimensions of the word vector was used in this research, there are 50, 60, 100, 150, 200, and 500, this tuning parameter is used to determine their effect on the resulting accuracy. The best accuracy around 88.17% is obtained at the word vector 100 dimension and the lowest accuracy is 85.86% at the word vector 500 dimension.

Hybrid node‐based tensor graph convolutional network for aspect‐category sentiment classification of microblog comments

SummaryAspect‐category sentiment classification of microblog comments aims to identify the sentiment polarity of different opinion aspects in microblog comments, which is meaningful for the analysis of public opinion. At present, most of aspect‐category sentiment classification methods need much annotation data, and regard comments as independent samples, without using of the relationship between comments. This article proposes an aspect‐category sentiment classification method based on tensor graph convolutional networks. First, the combination of a comment and its aspect category is regarded as a hybrid node, and the original representation of a hybrid node is encoded by the Bert model. Second, sentiment graph and semantic graph are constructed according to the semantic similarity and sentimental relevance between hybrid nodes, and they are stacked into a tensor. Then two convolution operations, including intra‐graph convolution and inter‐graph convolution, are performed for each layer of graph tensor. In this way, hybrid nodes can learn and merge the heterogeneous information of different graphs. Finally, under the supervision of few labeled comments, the sentiment classification can be completed based on the features of the hybrid nodes. Experimental results on two microblog datasets show that the proposed model can significantly improve the performance of sentiment classification compared with other baseline models.