Problem Of Sentiment Analysis Research Articles

Bridging the Language Gap: Evaluating Text Data Pre-processing and Classification Techniques in Urdu Sentiment Analysis

The advancement of the internet and rapid growth in social media platforms such as Facebook, X , Instagram and others which made it possible for information on review on goods, reaction on events, services from experts, and political beliefs to be easily and globally shared within no time. Due to this fast propagation of information may have both positive and negative impact on thinking and ability to take decision on a particular event or anything related. Due to the generation of such a huge number of date and while the volume of studies directed on slant investigation is quickly extending, these examinations for the most part address English language concerns. But the English the not the only language by which data is got propagated but other such as Urdu, Farsi, Hindi, Pasto, branch etc. The main goal of this research study is to critically assess the data related to Urdu and the Urdu sentiment analysis's progress and problems and present solutions. After critically reviewing the literature and related work following two research directions are identified in accordance with our study— the first one is text pre-processing and the other is sentiment classification. Here, we had the opportunity to describe the progress that has been made in this research field. The pre-processing steps include Tokenisation, stop words removal, word segmentation, text cleaning, transforming into numeric vectors and others then applying machine learning models i.e. regression and SVM. Result is compared out for each of the model use. After thorough investigation, the following results demonstrated that the LDA-TFID model achieved the highest accuracy at 0.923977 as compared to LDA-BOW and SVM-BOW and also outperforming both LDA-BOW and SVM-BOW in term of efficiency and accuracy. This indicates the effectiveness of TF-IDF in enhancing model performance for Urdu sentiment analysis.

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia

<span lang="EN-US">Global recession news dominates social media, particularly in Indonesia, with social news platforms on Twitter generating public responses and <br /> re-tweetings on the issue. Mining these opinions from Twitter using a sentiment analysis approach yields invaluable insights. The research stages included data collection, pre-processing, data labeling using the <br /> lexical-based method like valence aware dictionary and sentiment reasoner (VADER) and TextBlob, sampling techniques using synthetic minority oversampling technique (SMOTE) and random over sampling (ROS) before and after splitting data, and modeling using machine learning such as support vector machines (SVM), k-nearest neighbour (KNN), naive Bayes, and model evaluation. The problem is that almost 300,000 data collected from NodeXL are unbalanced. The findings show that models with balanced datasets show better model evaluation results. The sampling technique was carried out before and after splitting the data. The model evaluation results show that the Bernoulli-naive Bayes algorithm, with the VADER labeling technique, and the SMOTE sampling technique after splitting data, obtains the best accuracy of 84%, and using the ROS technique obtains an accuracy of 81%. On the other hand, with the SMOTE and ROS technique before splitting data on the SVM algorithm, it gets the best accuracy of 93% from before if only using SVM only reached 84%.</span>

Prototyping a Reusable Sentiment Analysis Tool for Machine Learning and Visualization

Evaluating customer satisfaction is very significant in all organizations to get the perspective of users/customers/stakeholders on products and/or services. Part of the data obtained during the evaluation are observations and comments of respondents and these are very rich in insights as they provide information on the strengths as well as the areas needing improvement. As the volume of textual data increases, the difficulty of analyzing them manually also increases. With these concerns, text analytics tools should be used to save time and effort in analyzing and interpreting the data. The textual data being processed in sentiment analysis problems vary in so many ways. For instance, the context of textual data and the language used vary when data are sourced from different locations and areas or fields. Thus, machine learning was utilized in this study to customize text analysis depending on the context and language used in the dataset. This research aimed to produce a prototype that can be used to explore three vectorization techniques and selected machine learning algorithms. The prototype was evaluated in the context of features for the application of machine learning in sentiment analysis. Results of the prototype development and the feedback and suggestions during the evaluation were presented. In future work, the prototype shall be improved, and the evaluators' feedback will be considered.

Real-Time Sentiment Analysis

Natural Language Processing (NLP) is a computerized method to text interpretation that is founded on a set of ideas as well as a set of technology. This is a very active field of study and development, there is no universally accepted definition that satisfies everyone, but there are several elements that any intelligent person would consider a person's interpretation. Sentiment analysis or opinion mining is one of the important aspects of NLP (Natural Language Processing). In recent years, sentiment analysis has received a lot of attention. Sentiment analysis has a wide range of applications, including social media analytics, which simply means creating opinions for individuals on social media by analyzing their sentiments or ideas, which they express through text. It is also an example of how you can review customer feedback and responses. Thus identify the negative comments and reasons why the customers have issues with your product or service. Sentiment analysis enables you to respond to matters promptly before the customer leaves you altogether! In this paper, we aim to tackle the problem of Real-Time sentiment analysis, which is one of the dominant applications of sentiment analysis. Real-time sentiment analysis is an AI-powered solution to analyze the input provided by the user with the help of a pre-trained Machine Learning model. We have used a dataset of 1600000 tweets with 6 different attributes to build our model with the help of machine learning algorithms such as Naïve Bayesian classifier, Neural networks, and VADER. This paper is about the techniques and machine learning algorithms used to analyze the effectiveness of such models. Keywords: Sentiment Analysis; Machine Learning Algorithms; Naïve Bayesian classifier; Neutral Networks; VADER.

Movie sentiment analysis based on Long Short-Term Memory Network

An important task in the study of Natural Language Processing (NLP) is the analysis of movie reviews. It finishes the task of classifying movie review texts into sentiment, such as positive, negative or neutral sentiment. Previous works mainly follow the pipeline of LSTM (Long Short-Term Memory Network). The network model is a variant of Recurrent Neural Network (RNN) and particularly suitable for processing natural language texts. Though existing LSTM-based works have improved the performance significantly, we argue that most of them deal with the problem of analyzing the sentiment of movie reviews while ignore analyze the model performance in different application scenarios, such as different lengths of the reviews and the frequency of sentiment adverbs in the reviews. To alleviate the above issue, in this paper, we constructed a simple LSTM model containing an embedding layer, a batch normalization layer, a dropout layer, a one-dimensional convolutional layer, a maximal pooling layer, a bi-directional LSTM layer and a fully connected layer. We used the existing IMDB movie review dataset to train the model, and selected two research scenarios of movie review length and frequency of occurrence of sentiment adverbs to test the model, respectively. From the experimental results, we proposed a model for the scenarios in which the LSTM model handles the problem of sentiment analysis with respect to the dataset construction, model stability and generalization ability, text fragment processing, data preprocessing and feature extraction, model optimization and improvement.

Comparison of Multinomial Naïve Bayes and Bernoulli Naïve Bayes on Sentiment Analysis of Kurikulum Merdeka with Query Expansion Ranking

Social media is one of the public services for conveying or obtaining news, opinions and comments on an issue. One of the social media that is in great demand by the people of Indonesia is Twitter. Kurikulum merdeka is one of the most discussed issues currently on Twitter. Kurikulum merdeka is a curriculum that incorporates varied intra-curricular learning with more optimal content to provide students adequate time to investigate ideas and build expertise. Until now, kurikulum merdeka still reaps the pros and cons. To process and analyze further regarding opinions on the kurikulum merdeka, it can be done using sentiment analysis. The high dimension of features in the classification process becoming a problem in sentiment analysis because it causes classification to be inefficient, so feature selection is needed to solve this problem. The purpose of this study was to obtain the results of the classification of kurikulum merdeka sentiments using the multinomial naïve bayes and bernoulli naïve Bayes, as well as query expansion rankings for feature selection and to compare the performance of the two classifications. Multinomial naïve bayes classification produces 106 tweets with positive sentiment and 164 tweets with negative sentiment with accuracy, recall, precision and f-measure respectively 98.889%, 98.131%, 99.057% and 98.591%, while bernoulli naïve bayes produces 95 tweets with positive sentiment and 175 tweets with negative sentiment with accuracy, recall, precision, and f-measure respectively 94.815%, 87.850%, 98.947% and 93.069% respectively. Therefore, multinomial naïve bayes classifies the kurikulum merdeka sentiment better than bernoulli naïve bayes.

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks are introduced for analyzing different sentiment elements and their relations, including the aspect term, aspect category, opinion term, and sentiment polarity. Unlike early ABSA works focusing on a single sentiment element, many compound ABSA tasks involving multiple elements have been studied in recent years for capturing more complete aspect-level sentiment information. However, a systematic review of various ABSA tasks and their corresponding solutions is still lacking, which we aim to fill in this survey. More specifically, we provide a new taxonomy for ABSA which organizes existing studies from the axes of concerned sentiment elements, with an emphasis on recent advances of compound ABSA tasks. From the perspective of solutions, we summarize the utilization of pre-trained language models for ABSA, which improved the performance of ABSA to a new stage. Besides, techniques for building more practical ABSA systems in cross-domain/lingual scenarios are discussed. Finally, we review some emerging topics and discuss some open challenges to outlook potential future directions of ABSA.

Emotion quantification and classification using the neutrosophic approach to deep learning

Advancements in the rapidly evolving specialization of deep learning have aided in improving several natural language understanding tasks. Sentiment and emotion classification models have improved, but when it comes to fine-grained sentiment analysis, these models can perform better. Human sentiment in natural language is generally an intricate combination of emotions, which can sometimes be indeterminate, neutral, or ambiguous. In the case of fine-grained sentiment analysis, the sentiments can be very similar to each other and interconnected, e.g., anger and fear. Most deep learning systems try to solve the problem of fine-grained sentiment analysis as a classification problem. However, fine-grained sentiments might combine similar emotions with one primary emotion. Trying to solve the problem as a classification task can result in better performance on benchmarks but does not ensure a better understanding and representation of language. The proposed work explores applying neutrosophy for fine-grained sentiment analysis using large language models. Neutrosophy identifies neutralities and employs membership functions (neutral, positive, negative) to quantify an instance into Single Valued Neutrosophic Sets (SVNS). This paper introduces Refined Emotion Neutrosophic Sets (RENS) for emotions (with four emotions) and Refined Ekman’s Emotion Neutrosophic Sets (REENS) with seven emotions. In this paper, refined neutrosophic sets with membership functions are employed for each sentiment across a given taxonomy and assigned their values using the Neutrosophic Iterative Neural Clustering (NINC) algorithm proposed in this paper. It facilitates not only classifying sentiments but also quantifying the presence of each sentiment present in a given sample. It aids in better understanding and representation of samples across multiple sentiments, as in fine-grained sentiment analysis, experiments are performed on the GoEmotions dataset. The proposed approach performs on par with cross-entropy deep learning classifiers and is reproducible across different pre-trained language models.

REVIEW OF METHODS FOR DETERMINING THE TONATION OF TEXTS IN NATURAL LANGUAGES

The analysis of sentiment in user comments finds application in many areas, such as evaluating the quality of goods and services, analyzing emotions in messages, and detecting phishing advertisements. There are numerous methods for analyzing the sentiment of textual data in the Russian language, but automatic sentiment analysis of Russian-language texts is much less developed than for other major world languages. This article is part of a broader study on the creation of an information system for detecting dangerous content in the cyberspace of Kazakhstan. The purpose of this article is to provide an analytical review of the different approaches to sentiment analysis of Russian-language texts and to compare modern methods for solving the problem of text classification. Additionally, the article seeks to identify development trends in this area and select the best algorithms for use in further research. The review covers different methods for text data preprocessing, vectorization, and machine classification for sentiment analysis of texts, and it concludes with an analysis of existing databases on this topic. The article identifies some of the main unresolved problems in sentiment analysis of Russianlanguage texts and discusses planned further research.

Attention Mechanism Architecture for Arabic Sentiment Analysis

This article tackles the problem of sentiment analysis in the Arabic language where a new deep learning model has been put forward. The proposed model uses a hybrid bidirectional gated recurrent unit (BiGRU) and bidirectional long short-term memory (BiLSTM) additive-attention model where the Bidirectional GRU/LSTM reads the individual sentence input from left to right and vice versa, enabling the capture of the contextual information. However, the model is trained on two types of embeddings: FastText and local learnable embeddings. The BiLSTM and BiGRU architectures are put into competition to identify the best hyperparameter set for the model. The developed model has been tested on three large-scale commonly employed Arabic sentiment dataset: large-scale Arabic Book Reviews Dataset (ABRD), Hotel Arabic-Reviews Dataset (HARD), and Books Reviews in the Arabic Dataset (BRAD). The testing results demonstrate that our model outperforms both the baseline models and the state-of-the-art models reported in the original references of these datasets, achieving accuracy scores of 98.6%, 96.19%, 95.65% for LARB, HARD, and BRAD, respectively. Furthermore, to demonstrate the generalization capabilities of our model, the performances of the model have been evaluated on three other natural language processing tasks: news categorization, offensive speech detection, and Russian sentiment analysis. The results demonstrated the developed model is language- and task-independent, which offers new perspectives for the application of the developed models in several other natural language processing challenges.

An enhanced approach for sentiment analysis based on meta-ensemble deep learning

Sentiment analysis, commonly known as “opinion mining,” aims to identify sentiment polarities in opinion texts. Recent years have seen a significant increase in the acceptance of sentiment analysis by academics, businesses, governments, and several other organizations. Numerous deep-learning efforts have been developed to effectively handle more challenging sentiment analysis problems. However, the main difficulty with deep learning approaches is that they require a lot of experience and hard work to tune the optimal hyperparameters, making it a tedious and time-consuming task. Several recent research efforts have attempted to solve this difficulty by combining the power of ensemble learning and deep learning. Many of these efforts have concentrated on simple ensemble techniques, which have some drawbacks. Therefore, this paper makes the following contributions: First, we propose a meta-ensemble deep learning approach to improve the performance of sentiment analysis. In this approach, we train and fuse baseline deep learning models using three levels of meta-learners. Second, we propose the benchmark dataset “Arabic-Egyptian Corpus 2” as an extension of a previous corpus. The corpus size has been increased by 10,000 annotated tweets written in colloquial Arabic on various topics. Third, we conduct several experiments on six benchmark datasets of sentiment analysis in different languages and dialects to evaluate the performance of the proposed meta-ensemble deep learning approach. The experimental results reveal that the meta-ensemble approach effectively outperforms the baseline deep learning models. Also, the experiments reveal that meta-learning improves performance further when the probability class distributions are used to train the meta-learners.

Chinese sentiment analysis model by integrating multi-granularity semantic features

PurposeIn recent years, Chinese sentiment analysis has made great progress, but the characteristics of the language itself and downstream task requirements were not explored thoroughly. It is not practical to directly migrate achievements obtained in English sentiment analysis to the analysis of Chinese because of the huge difference between the two languages.Design/methodology/approachIn view of the particularity of Chinese text and the requirement of sentiment analysis, a Chinese sentiment analysis model integrating multi-granularity semantic features is proposed in this paper. This model introduces the radical and part-of-speech features based on the character and word features, with the application of bidirectional long short-term memory, attention mechanism and recurrent convolutional neural network.FindingsThe comparative experiments showed that the F1 values of this model reaches 88.28 and 84.80 per cent on the man-made dataset and the NLPECC dataset, respectively. Meanwhile, an ablation experiment was conducted to verify the effectiveness of attention mechanism, part of speech, radical, character and word factors in Chinese sentiment analysis. The performance of the proposed model exceeds that of existing models to some extent.Originality/valueThe academic contribution of this paper is as follows: first, in view of the particularity of Chinese texts and the requirement of sentiment analysis, this paper focuses on solving the deficiency problem of Chinese sentiment analysis under the big data context. Second, this paper borrows ideas from multiple interdisciplinary frontier theories and methods, such as information science, linguistics and artificial intelligence, which makes it innovative and comprehensive. Finally, this paper deeply integrates multi-granularity semantic features such as character, word, radical and part of speech, which further complements the theoretical framework and method system of Chinese sentiment analysis.

Deep Learning Using Context Vectors to Identify Implicit Aspects

Aspects extraction is the key task in the sentiment analysis problem, which includes extraction of both explicit and implicit aspects. Identifying implicit aspects is not a new task in sentiment analysis, but it still presents many challenges. Numerous studies have addressed the issue and offered different approaches, but there are still fundamental challenges as follows: solving the context word problem; the domain-specific problem; and the implicit aspects represented by sentiment words that do not appear explicitly in the text. This paper proposes a method for identifying implicit aspects of sentiment words. To address these above challenges, the system is built on the foundation of deep learning with context vectors, dependency grammar, anaphora coreference resolution, and sentiment ontology. The dependency parser and the entity coreference resolver are adopted to filter out the sentiment–aspect pairs data generated from embedding context words. Then, a fine-tuned technique is applied, and the classifier corresponding to each sentiment word is built to identify implicit aspects. This combination has produced positive results, reaching almost 90% accuracy.

Sentiment analysis model for Airline customers’ feedback using deep learning techniques

Sentiment analysis (SA) has recently developed an automated approach for assessing sentiment, emotion, and these reviews or opinions to extract relevant and subjective information from text-based data. Analyzing sentiment on social networks, such as Twitter, has become a powerful means of learning about the users’ opinions and better understanding and satisfaction. However, it consumes time and energy to disperse and collect surveys from clients, often inaccurate and inconsistent, and evaluating and improving the accuracy of the methods in sentiment analysis is being hindered by the challenges encountered in Natural Language Processing (NLP). This paper uses NLP, text analysis, biometrics, and computational linguistics to detect and extract replies, moods, or emotions from Skytrax Airline Customers' Feedback SACF data. This research uses deep learning models to analyze various approaches applied to small SACF to solve sentiment analysis problems. We applied word embedding (Glove embedding models) to improve the sentiment classification performance of a series of datasets extensively utilized for feature extractions. Finally, a comparative study has been conducted on the SACF data analysis utilizing deep learning (DL)for evaluating the performance of the different models and input features, which is Recurrent Neural Networks (RNN), long short-term memory (LSTM), Gated Recurrent Unit (GRU), 1D Convolutional Neural Networks (CONV1D), and Bidirectional Encoder Representations from Transformers (BERT) for application to big datasets in 2019. This approach was assessed using each classification technique; the precision, recall, f1-score, and accuracy metrics for sentiment analysis have been identified. And The results show that LSTM outperforms in classification accuracy; the outcome was 91%.

Machine Learning Algorithms for Natural Language Processing Tasks: A Case of COVID-19 Twitter data (Thailand)

This paper presents the use of natural language processing for the problem of information extraction and sentiment analysis. The dataset is from Twitter that has the information of people mentioning about COVID- 19, this study has two tasks: (i) classification approach for information extraction task and (ii) deep learning approach for sentiment analysis task. In information extraction task, the data was gathered from twitter that related to COVID-19 information, and the sequence labelling method applied to classify text before giving it to classification algorithms (K-NN, Naïve Bayes, Decision Tree, Random Forest, and SVM). In sentiment analysis task, data was classified by convert the word into index and using word embedding, then to process deep learning algorithm (Bi-directional GRU). The accuracy of two tasks are 98% and 79% respectively.

Application of the text wave model to the sentiment analysis problem

Authors researched the wave model of text representation which is one of the implementations of distributive semantics. This model takes into account not only the frequency of words occurrence in the text, but also their mutual location. The purpose of the study: to increase the accuracy of the analysis of the tonality of short texts based on the wave model. The method of determining the relationship between the text and the term is based on the calculation of the probability amplitude of the text and term proximity using a wave model. The term with the highest probability amplitude is considered to correspond most closely to the meaning of the text. The wave model allowed taking into account the fact that well-known methods define antonyms as semantically close lexical units. For the experimental study of this technique, a solution to the problem of sentiment analysis was chosen, exactly, finding the correspondence of user reviews about the product to the classes “positive” and “negative”. As a result, the accuracy of the text tonality defining was obtained up to 76.4 %, which exceeds the accuracy of the classical approach as well as the well-known methods of sentiment analysis for the Russian language. In addition, authors detected significant influence on classification accuracy of such model parameters as the choice of a basic distributive semantic model, the choice of a control point for calculating wave numbers, taking into account the influence of antonyms. The presented model has shown high accuracy in identifying the relationships of the text with concepts that are not explicitly present in it and can be successfully used as a mathematical basis for solving problems of sentiment analysis. In addition, the results obtained indicate the potential use of the wave model in other areas that require the classification of texts by indirect signs, for example, to determine the elements of author psychological portrait.

Naïve Bayes dan Support Vector Machine Berbasis PSO untuk Seleksi Fitur pada Sentiment Analysis

Sentiment analysis is a process that aims to determine the content of the dataset in the form of positive, negative and neutral text. Currently the opinion of the general public is an important source of decision making. Social media is a place to express public opinion on an object, problem or event. Such as the government's policy regarding the relocation of the capital city of Indonesia, which was originally in Jakarta to Kalimantan, did not escape the attention of the public, especially Twitter users. One of the problems in sentiment analysis is the high number of attributes and dimensions in the dataset. In this study, sentiment analysis was carried out on the relocation of the national capital using the Naïve Bayes method, and the Support Vector Machine based on Particle Swarm Optimization (PSO). The advantages of the Support Vector Machine are High dimensional space and Vector document space. Feature selection greatly affects the performance of the classification, the use of PSO as feature selection to improve accuracy. The results of this study obtained the best accuracy value of 96.45% and the AUC value of 0.920 from the application of PSO on the Support Vector Machine. This result has increased when compared to the experimental results using Naïve Bayes and Support Vector Machine without PSO. The application of PSO on the Support Vector Machine is proven to get better accuracy results in predicting sentiment analysis on the dataset of moving the State Capital.

Analysis of psychological characteristics and emotional expression based on deep learning in higher vocational music education.

Sentiment analysis is one of the important tasks of online opinion analysis and an important means to guide the direction of online opinion and maintain social stability. Due to the multiple characteristics of linguistic expressions, ambiguity, multiple meanings of words, and the increasing speed of new words, it is a great challenge for the task of text sentiment analysis. Commonly used machine learning methods suffer from inadequate text feature extraction, and the emergence of deep learning has brought a turnaround for this purpose. In this paper, we investigate the problem of text sentiment analysis using methods related to deep learning. In order to incorporate user and product information in a more diverse way in the model, this paper proposes a model based on a deep bidirectional long-and short-term memory network-self-attention mechanism-custom classifier. The model first identifies contextual associations and acquires deep text features through a deep bidirectional long- and short-term memory network and then captures important features in the text using a self-attentive mechanism. The model finally combines user information and product information to build a custom classifier module and uses context-aware attention mechanisms to assign specific parameters to user information and product information, which improves the performance of the model on public datasets compared with current common models. The results show that the accuracy of the algorithm in this paper is high, and it is about 5% lower than the traditional algorithm. The method can reduce the number of iterations and the running time of the algorithm.

Exploring Multi-lingual, Multi-task, and Adversarial Learning for Low-resource Sentiment Analysis

Deep learning has become most prominent in solving various Natural Language Processing (NLP) tasks including sentiment analysis. However, these techniques require a considerably large amount of annotated corpus, which is not easy to obtain for most of the languages, especially under the scenario of low-resource settings. In this article, we propose a deep multi-task multi-lingual adversarial framework to solve the resource-scarcity problem of sentiment analysis by leveraging the useful and relevant knowledge from a high-resource language. To transfer the knowledge between the different languages, both the languages are mapped to the shared semantic space using cross-lingual word embeddings. We evaluate our proposed architecture on a low-resource language, Hindi, using English as the high-resource language. Experiments show that our proposed model achieves an accuracy of 60.09% for the movie review dataset and 72.14% for the product review dataset. The effectiveness of our proposed approach is demonstrated with significant performance gains over the state-of-the-art systems and translation-based baselines.

Kurdish Language Sentiment Analysis: Problems and Challenges

The increasing usage of blogs, social networks, and forums for sharing opinions on a certain topic has created vast amounts of internet data. Therefore, Sentiment Analysis has gained great popularity among researchers and industry for analyzing the polarity of users' opinions. In recent years, Sentiment Analysis has been applied to various languages using machine learning-approach, corpus-based approach, and deep learning techniques since it is beneficial for creating an effective recommender system. The Kurdish Language is an Indo-European language, one of the official languages in Iraq, and it is also widely used in Turkey, Iran, and Syria. Although the importance of this Language is spoken by over 40 million people, to the best of our knowledge, no research has been done regarding the challenges and problems of Kurdish sentiment analysis. Our research aims to highlight the latest studies and examine the most critical challenges of applying sentiment analysis approaches to the Kurdish Language. The study includes determining each challenge in each step of sentiment analysis processing in the Kurdish Language. In addition, our proposed methodology that could help address most of these challenges is implementing a hybrid approach by combining machine learning and lexicon-based approaches to improve the proficiency of sentiment classification in the Kurdish Language.