Perform Sentiment Analysis Research Articles

A Deep Learning based Model using Review Associated Feature Extraction Approach for Sentiment Analysis

With the advancement of internet technologies, in the present days, online forums, social media platforms and e-commerce sites have made the product reviews process very easy. There are a lot of mobile applications, websites and forums where consumers used to share and circulate their opinions, experiences, ideas and views regarding products, brands and services. In consequence, online user reviews have become a deciding factor for many consumers prior to purchasing their selected items. The sentiment analysis is a technique to extract sentiments, feelings and insights from customer reviews and public texts. Therefore, plenty of businesses perform sentiment analysis in order to more thoroughly comprehend of their customer opinions and suggestions regarding their products and services. Furthermore, a number of scientific researchers also have a keen interest in classifying customer reviews into a set of labels employing text classification techniques. The objective of the this research work is to develop an approach to extract review associated features using Part-of-Speech (POS) tagging and design a CNN model to classify the reviews' sentiment as positive or negative. In this paper, an approach to extract review associated feature has been presented. Natural Language Processing (NLP) techniques are utilized for data preprocessing to remove uninformative data from reviews. Deep learning model CNN is used for sentiment classification and Amazon mobile reviews dataset is used for the experiment. The proposed model is experimentally evaluated and provides enhanced performance than other models also provides improved accuracy of 97.23% on Amazon mobile review dataset.

Comparison of RNN Architectures and Non-RNN Architectures in Sentiment Analysis

This study compares the sentiment analysis performance of multiple Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks. THE METHODS EVALUATED ARE simple Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Recurrent Neural Network, and 1D ConvNets. A dataset comprising text reviews with positive or negative sentiment labels was evaluated. All evaluated models demonstrated an extremely high accuracy, ranging from 99.81% to 99.99%. Apart from that, the loss generated by these models is also low, ranging from 0.0043 to 0.0021. However, there are minor performance differences between the evaluated architectures. The Long Short-Term Memory and Gated Recurrent Unit models mainly perform marginally better than the Simple Recurrent Neural Network, albeit with slightly lower accuracy and loss. In the meantime, the Bidirectional Recurrent Neural Network model demonstrates competitive performance, as it can effectively manage text context from both directions. Additionally, One-Dimensional Convolutional Neural Networks provide satisfactory results, indicating that convolution-based approaches are also effective in sentiment analysis. The findings of this study provide practitioners with essential insights for selecting an appropriate architecture for sentiment analysis tasks. While all models yield excellent performance, the choice of architecture can impact computational efficiency and training time. Therefore, a comprehensive comprehension of the respective characteristics of Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks is essential for making more informed decisions when constructing sentiment analysis models.

Analysing the Impact of Sentiment Analysis on Predicting Movie Success in Social Network Communities: A Review

Abstract: Today’s online world was fully filled up with blogs, views, comments, and posts through various websites and social surfaces. Sentiment analysis has emerged as a valuable tool in the film industry for automatically categorizing the polarity of thoughts expressed in movie reviews. By analyzing language patterns, sentiment analysis can determine whether a review contains a positive or negative assessment of a particular movie. This approach allows for both objective and subjective methods to predict the success of a movie. With this analysis, it is possible to evaluate audience reactions and identify trends in movie reviews, which can be valuable for filmmakers and movie studios seeking to gauge public opinion and make informed decisions about future projects. In order to perform sentiment analysis on movie reviews, machine learning algorithms are usually applied after natural language processing techniques have been used to extract pertinent features from the text, such as sentimentbearing words and phrases. Objective methods use statistical and machine learning techniques to analyze past movie data, such as budget, genre, and release date, to predict box office revenues.

Global recession sentiment analysis utilizing VADER and ensemble learning method with word embedding

The issue of the Global Recession is hitting various countries, including Indonesia. Many Indonesians have expressed their opinions on the issue of the global recession in 2023, one of which is from Twitter. By understanding public sentiment, we can assess the impact felt by the public on the issue itself. Sentiment analysis in this research is a form of support to evaluate Indonesia's sustainability in dealing with the issue of Global Recession in accordance with the Sustainable Development Goals (SDGs). However, in previous research, it is still rare to find a model that has good performance in conducting Global Recession Sentiment Analysis. Therefore, the purpose of this research is to propose a machine learning model that is expected to provide good performance in sentiment analysis. The existing sentiment dataset is labeled with the Valence Aware Dictionary for Social Reasoning (VADER) algorithm, then an Ensemble Learning method is designed which is composed of Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms. After that, the Countvectorizer feature extraction with N-Gram, Best Match 25 (BM25), and Word Embedding is carried out to convert sentences in the dataset into numerical vectors so as to improve model performance. The research results provide a more optimal accuracy performance of 95.02% in classifying sentiment. So that the proposed model successfully performs sentiment analysis better than previous research.

Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine

In the current digital era, sentiment analysis has become an effective method for identifying and interpreting public opinions on various topics, including public health issues such as COVID-19 vaccination. Vaccination is a crucial measure in tackling this pandemic, but there are still a number of people who are skeptical and reluctant to receive the COVID-19 vaccine. This public perception is largely influenced by, including information received from social media and online platforms. Therefore, sentiment analysis of the COVID-19 vaccine is one way to understand the public's perception of the COVID-19 vaccine. This research has the purpose to enhance the classification performance in sentiment analysis of COVID-19 vaccines by implementing Information Gain Ratio (IGR) and Particle Swarm Optimization (PSO) on the Support Vector Machine (SVM). With a dataset of 2000 entries consisting of 1000 positive labels and 1000 negative labels, validation was performed through a combination of data splitting with an 80:20 ratio and stratified 10-Fold cross-validation. Applying the basic SVM, an accuracy of 0.794 and an AUC value of 0.890 were obtained. Integration with Information Gain Ratio (IGR) feature selection improved the accuracy to 0.814 and an AUC of 0.907. Furthermore, through the combination of SVM based on PSO and IGR, the accuracy significantly improved to 0.837 with an AUC of 0.913. These results demonstrate that the combination of feature selection techniques and parameter optimization can enhance the performance of sentiment classification towards COVID-19 vaccines. The conclusions drawn from this research indicate that the integration of IGR and PSO positively contributes to the effectiveness and predictive capability of the SVM model in sentiment classification tasks.

AsCDPR: a novel framework for ratings and personalized preference hotel recommendation using cross-domain and aspect-based features

PurposeThe explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.Design/methodology/approachWe propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.FindingsExpanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.Research limitation/implicationsThis study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.Originality/valueThis study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

Lexicon-based sentiment analysis for Kannada-English code-switch text

Sentiment analysis is the process of computationally recognizing and classifying the attitudes conveyed in each text towards a particular topic, product, etc. which is either positive or negative. Sentiment analysis is one of the interesting applications of natural language processing (NLP) and which is used to analyze the social media. Text in social media is casual and it can be written either in code-switch or monolingual text. Several researchers have implemented sentiment analysis on monolingual text, though sentiments can be expressed in code-switch text. Sentiment analysis can be applied through deep learning (DL), machine learning (ML), or a Lexicon-based approach. Machine learning (ML) and deep learning (DL) methods are time-consuming, computationally expensive, and need training data for analysis. Lexicon-based method does not require training data and requires less time to find the sentiments in comparison with ML and DL. In this paper, we propose the Lexicon-based approach (NBLex) to analyze the sentiments expressed in Kannada-English code-switch text. This is the first effort that targets to perform sentiment analysis in Kannada-English code-switch text using the Lexicon-based approach. The proposed approach performed with better accuracy of 83.2% and 83% of F1-score.

Context-Dependent Multimodal Sentiment Analysis Based on a Complex Attention Mechanism

Multimodal sentiment analysis aims to understand people’s attitudes and opinions from different data forms. Traditional modality fusion methods for multimodal sentiment analysis con-catenate or multiply various modalities without fully utilizing context information and the correlation between modalities. To solve this problem, this article provides a new model based on a multimodal sentiment analysis framework based on a recurrent neural network with a complex attention mechanism. First, after the raw data is preprocessed, the numerical feature representation is obtained using feature extraction. Next, the numerical features are input into the recurrent neural network, and the output results are multimodally fused using a complex attention mechanism layer. The objective of the complex attention mechanism is to leverage enhanced non-linearity to more effectively capture the inter-modal correlations, thereby improving the performance of multimodal sentiment analysis. Finally, the processed results are fed into the classification layer and the sentiment output is obtained using the classification layer. This process can effectively capture the semantic information and contextual relationship of the input sequence and fuse different pieces of modal information. Our model was tested on the CMU-MOSEI datasets, achieving an accuracy of 82.04%.

Machine Learning Based Twitter Sentiment Analysis and User Influence

The use of social media platforms, such as Twitter, has grown exponentially over the years, and it has become a valuable source of information for various fields, including marketing, politics, and finance. Sentiment analysis is particularly relevant in social media analysis. Sentiment analysis involves the use of natural language processing (NLP) techniques to automatically determine the sentiment expressed in a given text, such as positive, negative, or neutral.
 In this research paper, we focus on Twitter sentiment analysis and identify the most influential users in a given topic. We propose a methodology based on machine learning techniques to perform sentiment analysis and identify the most influential users on Twitter based on popularity. Specifically, we utilize a combination of NLP techniques, sentiment lexicons, and machine learning algorithms to classify tweets as positive, negative, or neutral. We then employ popularity calculations for each user to identify the top 10 most influential users on a given topic.
 The proposed methodology was tested on a large dataset of US airlines tweets which is related to a specific topic i.e. airlines, and the results show that the approach can effectively classify tweets according to sentiment and identify the most influential users. We evaluated the performance of several machine learning algorithms, including Multinomial Naive Bayes, Support Vector Machines (SVM), Decision Trees, Gradient Boosting, logistic regression, AdaBoost, KNN and Random Forest, and found that the logistic regression algorithm has achieved the highest accuracy.
 The proposed methodology has several implications for various fields, such as marketing, where sentiment analysis can help companies understand consumer behavior and tailor their marketing strategies accordingly. Moreover, identifying the most influential users can provide insights into opinion leaders in a given topic and help companies and policymakers target their messages more effectively.

YouTube Comment Scraping and Sentiment Analysis Using Python and Machine Learning

YouTube has become a massive platform for video sharing and discussion, with millions of users expressing their thoughts and opinions through comments. For content producers, marketers, and academics, gleaning significant insights from these comments can yield useful data. This project explores the combination of machine learning and web scraping techniques to scrape YouTube comments and perform sentiment analysis. By leveraging machine learning algorithms, we aim to analyze sentiments, opinions, and trends within the vast amount of textual data generated on the platform. The process involves collecting comments using the YouTube Data API or web scraping libraries, followed by preprocessing steps to clean and transform the data. Next, Machine learning methods like Naive Bayes, SVM, or deep learning models are used to train sentiment analysis models. These approaches include Bag-of-Words, TF-IDF, and word embedding. Our ability to comprehend the overall emotion conveyed by viewers is subsequently made possible by the trained models, which are then utilized to anticipate the sentiment of fresh comments. Additionally, the ability to identify very favorable or negative remarks is made possible by sorting comments based on sentiment scores. It is essential to consider the complexities of language and contextual nuances when conducting sentiment analysis. For many players in the YouTube ecosystem, this technique does provide useful insights into audience feedback, content strategy, and data-driven decision-making.

Managing Healthcare Infodemic by deep learning in providing healthcare services

Digital Health care data acquisition and processing is performed by Artificial Intelligence and Internet of Things technologies and digitization of data and information affects the patients’ behavior. News about COVID-19, a global pandemic, is circulating on social media worldwide providing a collection of big data. Awareness about the pandemic is spreading drastically in the form of messages, social media posts, tweets, and videos. It is, therefore, significant to assess the early flow of information on social media during the pandemic to prevent alarmism. This study aims to perform sentiment analysis of social media big data about COVID-19 by deep learning on a dataset provided by IEEE Data Port. The goal is to assist healthcare professionals in developing social media policies that can be used to change public opinion. The Dataset used consists of 11,858 COVID-19-related tweets collected on May 30, 2020. Data are labeled as positive or negative in the first step using TextBlob and VADER. In step II, various machine learning models are compared using three feature extraction techniques in combination with VADER and TextBlob. The results show that Extra Tree Classifier using TF-IDF features outperforms with an accuracy of 0.9474.

Sentiment Analysis of Tourist Attraction Review from TripAdvisor Using CNN and LSTM

The tourism sector has an important role in driving the economy. To find out the positive or negative responses of tourists, one of them is grouping through sentiment analysis using deep learning. The data used the tourist attraction dataset from TripAdvisor from several categories such as water and amusement park, nature, and museum. The methods used in this research are convolutional neural network (CNN) and long short-term memory (LSTM). In addition, Word2vec for feature extraction and Synthetic Minority Over-sampling (SMOTE) for handling imbalanced datasets will be used for this research. There are several scenarios used to perform sentiment analysis, with early stopping and with hyperparameter tuning using random search. The highest performance obtained on water and amusement park, nature, and museum category data is 83%, 97%, and 88% respectively for accuracy and 91%, 92%, and 93% respectively for F1-score. For the use of sentiment analysis methods, CNN can perform with the highest F1-score and LSTM can perform with the highest accuracy.

A prompt model with combined semantic refinement for aspect sentiment analysis

Recently, pre-trained language models (PLMs), especially pre-trained bidirectional encoder representations from transformers (BERT), have improved the performance of aspect-based sentiment analysis (ABSA) tasks to some extent. However, due to the imbalance of training data in different polarities, the following shortcomings remain in PLM-based ABSA methods: (1) for small corpus scenarios with polarized emotions, an unbalanced performance problem exists; and (2) for delicate and obscure scenes dominated by neutral emotions, PLM-based performance gains are limited. To address these shortcomings, we use BERT as an instance of PLMs to propose a general-purpose prompt model with combined semantic refinement for ABSA. First, we utilize a BERT without fine-tuning to automatically induce prompts for various ABSA datasets to enhance the adaptability of the model to different application scenarios. We then leverage multi-prompt learning to propose a data augmentation method to address the imbalance of training data in different polarities. Moreover, to further deepen the model's understanding and analysis of reviews with prompts, we also propose an improved BERT semantic refinement method that combines global semantic refinement and local semantic extraction. Experiments on five public datasets show that compared with existing methods, our macro-average F1 improvement is over 10% on polarized small datasets and over 7% on an emotionally delicate and obscure dataset.

A Multi-Channel Text Sentiment Analysis Model Integrating Pre-training Mechanism

The number of tourist attractions reviews, travel notes and other texts has grown exponentially in the Internet age. Effectively mining users’ potential opinions and emotions on tourist attractions, and helping to provide users with better recommendation services, which is of great practical significance. This paper proposes a multi-channel neural network model called Pre-BiLSTM combined with a pre-training mechanism. The model uses a combination of coarse and fine- granularity strategies to extract the features of text information such as reviews and travel notes to improve the performance of text sentiment analysis. First, we construct three channels and use the improved BERT and skip-gram methods with negative sampling to vectorize the word-level and vocabulary-level text, respectively, so as to obtain more abundant textual information. Second, we use the pre-training mechanism of BERT to generate deep bidirectional language representation relationships. Third, the vectors of the three channels are input into the BiLSTM network in parallel to extract global and local features. Finally, the model fuses the text features of the three channels and classifies them using SoftMax classifier. Furthermore, numerical experiments are conducted to demonstrate that Pre-BiLSTM outperforms the baselines by 6.27%, 12.83% and 18.12% in average in terms of accuracy, precision and F1-score.

Knowledge-Fusion-Based Iterative Graph Structure Learning Framework for Implicit Sentiment Identification.

Implicit sentiment identification is a significant classical task in text analysis. Graph neural networks (GNNs) have recently been successful in implicit sentiment identification, but the current approaches still suffer from two problems. On the one hand, there is a lack of structural information carried by the single-view graph structure of implicit sentiment texts to accurately capture obscure sentiment expressions. On the other hand, the predefined fixed graph structure may contain some noisy edges that cannot represent semantic information using an accurate topology, which can seriously impair the performance of implicit sentiment analysis. To address these problems, we introduce a knowledge-fusion-based iterative graph structure learning framework (KIG). Specifically, for the first problem, KIG constructs graph structures based on three views, namely, co-occurrence statistics, cosine similarity, and syntactic dependency trees through prior knowledge, which provides rich multi-source information for implicit sentiment analysis and facilitates the capture of implicit obscure sentiment expressions. To address the second problem, KIG innovatively iterates the three original graph structures and searches for their implicit graph structures to better fit the data themselves to optimize the downstream implicit sentiment analysis task. We compared our method with the mainstream implicit sentiment identification methods on two publicly available datasets, and ours outperformed both benchmark models. The accuracy, recall, and F1 values of KIG on the Pun of the Day dataset reached 89.2%, 93.7%, and 91.1%, respectively. Extensive experimental results demonstrate the superiority of our proposed method for the implicit sentiment identification task.

Sentiment Analysis on Movie Review from Rotten Tomatoes Using Modified Balanced Random Forest Method and Word2Vec

The film industry is one of the impacts of the rapid development of technology. This causes the film industry to increase every year. In addition, technological developments also affect the public to make it easier to access various movies from various websites. With many choices of movies, people need to know the quality of various movies by knowing the reviews of these movies from other people. However, the large number of audience reviews of a movie makes it difficult for people to categorize good movies and bad movies. The solution to the problem is to perform sentiment analysis on movie reviews. In this research, the classification method used is Modified Balanced Random Forest. This method was chosen because it can overcome imbalanced data and can increase accuracy and reduce time complexity. In this research, Word2Vec is also used as feature extraction. This feature extraction was chosen because previous research explained that Word2Vec has the advantage of being able to show the contextual similarity of two words in the resulting vector. The best model produced from this research is a model built without using stemming in the preprocessing stage, using 300 dimensions in Word2Vec, and using the Modified Balanced Random Forest classification method which produces an f1-score of 84.15%.

Cross-Modal Sentiment Analysis of Text and Video Based on Bi-GRU Cyclic Network and Correlation Enhancement

Cross-modal sentiment analysis is an emerging research area in natural language processing. The core task of cross-modal fusion lies in cross-modal relationship extraction and joint feature learning. The existing research methods of cross-modal sentiment analysis focus on static text, video, audio, and other modality data but ignore the fact that different modality data are often unaligned in practical applications. There is a long-term time dependence among unaligned data sequences, and it is difficult to explore the interaction between different modalities. The paper proposes a sentiment analysis model (UA-BFET) based on feature enhancement technology in unaligned data scenarios, which can perform sentiment analysis on unaligned text and video modality data in social media. Firstly, the model adds a cyclic memory enhancement network across time steps. Then, the obtained cross-modal fusion features with interaction are applied to the unimodal feature extraction process of the next time step in the Bi-directional Gated Recurrent Unit (Bi-GRU) so that the progressively enhanced unimodal features and cross-modal fusion features continuously complement each other. Secondly, the extracted unimodal text and video features taken jointly from the enhanced cross-modal fusion features are subjected to canonical correlation analysis (CCA) and input into the fully connected layer and Softmax function for sentiment analysis. Through experiments executed on unaligned public datasets MOSI and MOSEI, the UA-BFET model has achieved or even exceeded the sentiment analysis effect of text, video, and audio modality fusion and has outstanding advantages in solving cross-modal sentiment analysis in unaligned data scenarios.

Mental health at different stages of cancer survival: a natural language processing study of Reddit posts.

The purpose of this study was to use text-based social media content analysis from cancer-specific subreddits to evaluate depression and anxiety-loaded content. Natural language processing, automatic, and lexicon-based methods were employed to perform sentiment analysis and identify depression and anxiety-loaded content. Data was collected from 187 Reddit users who had received a cancer diagnosis, were currently undergoing treatment, or had completed treatment. Participants were split according to survivorship status into short-term, transition, and long-term cancer survivors. A total of 72524 posts were analyzed across the three cancer survivor groups. The results showed that short-term cancer survivors had significantly more depression-loaded posts and more anxiety-loaded words than long-term survivors, with no significant differences relative to the transition period. The topic analysis showed that long-term survivors, more than other stages of survivorship, have resources to share their experiences with suicidal ideation and mental health issues while providing support to their survivor community. The results indicate that Reddit texts seem to be an indicator of when the stressor is active and mental health issues are triggered. This sets the stage for Reddit to become a platform for screening and first-hand intervention delivery. Special attention should be dedicated to short-term survivors.

Automated Sentimental Analysis of Twitter Data

In these days, many intellectuals in the society provide their opinions and perspectives on various topics, products and thoughts through social media. Because of this in the market, the requirement of analysing the sentiment of data became more important and increasing day by day. When people using the media need information about a specific issue in the society and products available, it is very crucial to recognize the statements which make sense. For example, when purchasing a product, the consumer may want to know the details of the product, reviews that are provided about it. If they want to look for the drawbacks of the product, they prefer to get through the negative reviews, whereas when wanting to know the advantages, they prefer to check with positive reviews. This pattern can also be applied to national problems, political areas also. Due to large amount of data, manually analysing the data becomes almost impossible. Therefore, we require a model which has the ability to perform sentiment analysis in an automated manner and without any human intervention. Sentiment analysis can be done with the help of different algorithms. In this model, we use the Natural Language Processing (NLP) for conduction of the analysis and present performance comparisons in terms of accuracy and time taken. The model's graphical user interface (GUI) has been designed to enhance user-friendliness. It offers flexibility by allowing for training with any type of dataset of different size and types of data with the help Graphical user interface (GUI) provided and it also includes a module for testing the sentiment of input statements which are custom. The model shows its essence by providing the sentiment and nature of behind statements, making it to be applicable in various real-time scenarios. For example, it can be employed in to assess the product reviews in shopping platforms, providing an overview for the new consumers to understand the positives and negatives about the products. Additionally, it can be utilized for analysing the sentiments related to political areas. Since, there is lot of growth in number of people expressing their views on political issues through social media beyond print media we can grab lot of opinions on political leaders from the common people regarding their manifestos, schemes implemented, behaviour etc

Mô hình BiDAF trong bài toán phân tích cảm xúc

Sentiment analysis is a critical job in natural language processing. Controlling and evaluating customer feedback on their goods is a task that companies are especially interested in. For reading comprehension problems including attention processes, the BiDAF model is developed. Attention processes have recently been expanded and effectively used for natural language processing problems. In this study, we use the BiDAF model to perform sentiment analysis on Amazon product evaluations at the sentence level. The BiDAF model is a multilayered processing model that reflects context at multiple levels and uses the BiLSTM model. Furthermore, we investigate the sentence's attention weight distribution using the attention mechanism. With a recall measure, the model achieves an accuracy of up to 99.9%. We discovered that the attention weights of important phrases are equivalent to, if not higher than, the attention weights of sentiment words in the sentence.