Opinion Mining Framework Research Articles

IDEAL: an inventive optimized deep ensemble augmented learning framework for opinion mining and sentiment analysis

IDEAL: an inventive optimized deep ensemble augmented learning framework for opinion mining and sentiment analysis

Statistical Analysis and Accuracy Assessment of Improved Machine Learning Based Opinion Mining Framework

Sentiment analysis, also known as opinion mining, plays a crucial role in understanding and extracting valuable insights from textual data in various domains, including social media, customer feedback, and product reviews. This research presents an in-depth examination of an improved machine learning-based sentiment analysis framework, focusing on its statistical analysis and accuracy assessment. The research begins by introducing the framework's architecture, which incorporates advanced machine learning algorithms and natural language processing techniques. These enhancements aim to provide a more nuanced and context-aware sentiment analysis, addressing the limitations of traditional approaches. To evaluate the performance of the proposed framework, a comprehensive statistical analysis is conducted. Various statistical metrics, such as precision, recall, F1-score, and accuracy, are employed to assess its effectiveness in classifying text sentiments accurately. Additionally, the study explores the impact of different feature engineering and pre-processing techniques on model performance. The results of this study demonstrate the significant improvements achieved by the enhanced sentiment analysis framework in terms of accuracy and reliability. The statistical analysis confirms its superior performance in capturing subtle sentiment nuances, making it a valuable tool for applications requiring precise sentiment understanding. In conclusion, this research contributes to the field of sentiment analysis by presenting an improved machine learning-based framework and conducting a rigorous statistical assessment of its accuracy. The findings provide valuable insights for researchers and practitioners seeking to enhance sentiment analysis techniques and apply them effectively in various domains..

Computational analysis of dystopian elements in the partition fiction: A machine learning approach to the indian English novels

Despite the growth of digital humanities, the specific application of computational methods to analyse literary themes and elements remains underexplored. This study aims to use machine learning algorithms for text classification on seven novels about the partition of India. The article has a dual objective: firstly, to develop and implement an innovative opinion mining framework that leverages machine learning techniques to identify and classify dystopian elements in Partition novels; and secondly, to evaluate and compare the efficacy of the model in accurately classifying dystopian sentiments within selected literary texts. The proposed framework includes six phases of data collection, test-pre-processing, text extraction, text exploration, modelling or classification using machine leaning, and performance evaluation. Machine learning approaches such as Logistic Regression, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor are adopted to classify the text based on compiled dictionary. First, the study revealed that the main components and characteristics of dystopian elements found in selected narratives are ‘fear’, ‘suffer’, ‘oppression’ and ‘violence’. Second, the analysis showed that SVM with CountVectorizer, using imbalanced dataset and random oversampling dataset, outperforms other classifiers in classifying dystopian types in the selected novels. The results also suggest that CountVectorizer works better for the dataset compared to TF-IDF.

Perceptions of Hospital Care for Persons With Dementia During the COVID-19 Pandemic: A Social Media Sentiment Analysis.

The coronavirus disease 2019 (COVID-19) pandemic led to many hospital service disruptions and strict visitor restrictions that affected care of older adult populations. This study investigates perceptions of hospital care for persons with dementia during the COVID-19 pandemic as shared on Reddit's social media platform. This study combined an Opinion Mining Framework with linguistic processing to conduct a sentiment analysis of word clusters and care-based content in a sample of 1,205 posts shared between February 2020 and March 2023 in Reddit's English-language corpus. Data were classified based on reoccurring contiguous sequences of 2 words from our text sample. Hospital dementia care discourse on Reddit advanced 4 negative sentiment themes: (1) fear of poor medication management, hydration, and hygiene, (2) loss of patient advocacy, (3) precipitation of advance directive discussions, and (4) delayed discharge and loss of nursing home bed. One positive sentiment theme also emerged: gratitude toward hospital staff. Negative sentiment Reddit posts constituted a larger share of the posts than positive posts regarding hospital care for persons with dementia. People who posted about their experiences shared their concerns about hospital care deficiencies and the importance of including informal caregivers in hospital settings, particularly in the context of a pandemic. Implications exist for dementia training, improved quality of care, advance care planning, and transitions in care policies.

A spatiotemporal multi-feature extraction framework for opinion mining

With the rapid development of Internet technology and the explosive growth of digital text, opinion mining has become one of the important research hotspots in the field of natural language processing (NLP). In recent years, neural network based deep learning algorithms have been applied in the field of opinion mining. Considering the relation between temporal and spatial dimensions of text data and the characteristics of natural language itself, traditional deep learning algorithms cannot be comprehensive in the processing of fully feature extraction. In this paper, we propose a new deep learning framework for opinion mining, which includes a temporal feature extraction layer that consists of two layers of bidirectional simple recurrent unit (Bi-SRU) networks extracting features at the word and grammar levels; a semantic feature extraction layer that mainly contains a multi-head attention module; a spatial feature extraction layer with dilated convolution that is used to extract opinion preference features. The Internet movie database (IMDb) is used to verify the performance of the proposed framework. The experiment results show that the proposed framework can effectively improve the classification accuracy, whose performance is better than that of the compared algorithms.

Attention-based position-aware framework for aspect-based opinion mining using bidirectional long short-term memory

Aspect-based Opinion Mining is a form of fine-grained Sentiment Analysis and it models the semasiological relationship between aspect terms and context words in a sentence. The presence of a variety of context words has a significant impact on a sentence's sentiment polarity. As a result, while designing a model, it is necessary to consider the interaction of aspects and context words. Although existing approaches have taken into account an aspect’s position in a sentence, much of the research works have not explored the use of Sentiment Lexicons with the Deep Learning algorithms. In this paper, we propose a framework for an Attention-based position-aware Bidirectional Long Short-Term Memory network for Aspect-based Opinion Mining that incorporates a Sentiment Intensity Lexicon. The aspect word’s pre-trained vector is adjusted to be closer to semantically and sentimentally similar nearest neighbors and further away from sentimentally dissimilar neighbors. The proposed framework calculates aspect weights by concatenating the external knowledge in the form of lexicon sentiment intensity scores with word embeddings and position information. The framework experiments on the SemEval 2014 dataset. The results of the experiments illustrate that injecting external knowledge into the Bidirectional Long Short-Term Memory network can improve classification accuracy significantly.

Opinion mining from student text review for choosing better online courses

The process of online assessment has utilized get increased gradually as an evaluating tool in order to measure the lectures performance over Institution of Higher Learning (IHLs). However, these approaches generally have questionnaires sets consists of both quantitative and qualitative whereas the teaching assessment systems for several online lecture have concentrated on quantitative questions part due to its quick analysis. Contrarily, the part of qualitative has needed opinion from students that frequently ignored or misplaced and the opinion results level are omitted. This is due to student’s opinion which is generally in term of unstructured text that made hard to analyze the feedback manually. Moreover, the enormous data amount has produced on daily basis which can be utilized to opinion mining for extracting the student’s opinion in this exact online education area that has not widely applied in all educational system. Therefore, this paper has designed a framework by applying opinion mining concept to the student of coursera online machine learning course as the dataset whereas the comparison is accomplished using opinion mining framework to extract. The unstructured text gets processed and classification results based on polarity to obtain better outcome. Hence, this framework maintain several steps namely opinion extraction, unstructured text processing and polarity classification using NLP. Moreover, the performance of the outcome from the Opinion Mining (OM) gets evaluated using machine learning algorithms.

OpinionMine: A Bayesian-based framework for opinion mining using Twitter Data

This article studies opinion mining from social media with probabilistic logic reasoning. As it is known, Twitter is one of the most active social networks, with millions of tweets sent daily, where multiple users express their opinion about traveling, economic issues, political decisions etc. As such, it offers a valuable source of information for opinion mining. In this paper we present OpinionMine, a Bayesian-based framework for opinion mining, exploiting Twitter Data. Initially, our framework imports Tweets massively by using Twitter’s API. Next, the imported Tweets are further processed automatically for constructing a set of untrained rules and random variables. Then, a Bayesian Network is derived by using the set of untrained rules, the random variables and an evidence set. After that, the trained model can be used for the evaluation of new Tweets. Finally, the constructed model can be retrained incrementally, thus becoming more robust. As application domain for the development of our methodology we have selected tourism because it is one of the most popular topics in social media. Our framework can predict users’ intention to visit a place. Among the advantages of our framework is that it follows an incremental learning strategy. That is, the derived model can be retrained incrementally with new training sets thus becoming more robust. Further, our framework can be easily adapted to opinion mining from social media on other topics, whereas the rules of the derived model are constructed in an efficient way and automatically.

Opinion mining framework applied to a social networks data for small and medium enterprises

2) Objective: To present a framework for the mining of opinions that can be applied in the discovery of knowledge of the customers about to their experiences, based on unstructured data extracted from social networks, and that is applicable to the reality of small and medium enterprises.3) Methodology: This experimental research accessed data from the opinions of customers of four restaurants published in the social network TripAdvisor Brazil. The framework was based on the proposals formulated by Aranha (2007) and Feldman and Sanger (2007), techniques for Sentiment Analysis by Liu (2012) and Pang and Lee (2008) and Topic Modeling by Blei et al. (2012).4) Originality: The relevance consists in proposing a solution that is both accessible to SMEs and capable of processing opinions in Portuguese, something not very common in literature. Almost all similar applications in literature are dedicated to the English language.5) Main results: We highlight the generation of summaries and graphic visualizations that contribute to evidence knowledge about the relations between several expressions and terms that were not obvious. These allowed finding latent relationships between terms cited by different customers.6) Theoretical contributions: The methodological solution uses efficient and state-of-the-art techniques and methods to extract, process, and analyze customer opinions on the Internet quickly, efficiently, and economically.7) Social contributions: the framework developed presents an efficient, fast and economical way to mine data, presenting the results of the discovery of customer knowledge through the use of Sentiment Analysis and Topic Modeling techniques.

Penguin Search Optimization Based Feature Selection for Automated Opinion Mining

Twitter sentiment analysis is a vital concept in determining the public opinions about products, services, events or personality. Analyzing the medical tweets on a specific topic can provide immense benefits in medical industry. However, the medical tweets require efficient feature selection approach to produce significantly accurate results. Penguin search optimization algorithm (PeSOA) has the ability to resolve NP-hard problems. This paper aims at developing an automated opinion mining framework by modeling the feature selection problem as NP-hard optimization problem and using PeSOA based feature selection approach to solve it. Initially, the medical tweets based on cancer and drugs keywords are extracted and pre-processed to filter the relevant informative tweets. Then the features are extracted based on the Natural Language Processing (NLP) concepts and the optimal features are selected using PeSOA whose results are fed as input to three baseline classifiers to achieve optimal and accurate sentiment classification. The experimental results obtained through MATLAB simulations on cancer and drug tweets using k-Nearest Neighbor (KNN), Naïve Bayes (NB) and Support Vector Machine (SVM) indicate that the proposed PeSOA feature selection based tweet opinion mining has improved the classification performance significantly. It shows that the PeSOA feature selection with the SVM classifier provides superior sentiment classification than the other classifiers.

Enhanced Medical Tweet Opinion Mining using Improved Dolphin Echolocation Algorithm Based Feature Selection

Extraction and analysis of public opinions from social network data can provide interesting outcomes and inferences about product, service, event or personality. Twitter is one of the most popular medium for analyzing the public sentiment through user tweets. Feature specific opinion analysis provides highly accurate and effective classification and categorization of public opinions. This paper focuses on developing an opinion mining framework for automated analysis of tweet opinions using efficient feature selection and classification algorithms. For this purpose, an Improved Dolphin Echolocation Algorithm (IDEA) is developed by enhancing the optimization performance of the Dolphin Echolocation Algorithm (DEA). The limitations of DEA are the insufficient exploration and exploitation properties in local optimum solutions and also impact the convergence rate. These shortcomings are overcome by the proposed IDEA algorithm. In this work, first the tweets are collected and pre-processed to extract the features using Part-of-Speech (POS) tagging and n-grams aided by a dictionary. Using IDEA, the feature subset candidates are selected and the outcomes are fed as input to the baseline classifiers to obtain highly accurate opinion classification. The evaluation of the k-Nearest Neighbor (KNN), Naïve Bayes (NB) and Support Vector Machine (SVM) classifiers using the two feature selection approaches of DEA and IDEA are performed over cancer and drug tweets datasets and the results illustrate that the classification accuracy of opinions is enhanced significantly through the IDEA based feature selection than the traditional DEA algorithm. These results justify the utilization of the proposed IDEA algorithm for improving the opinion mining applications in different fields.

Opinion mining framework using proposed RB-bayes model for text classication

<p><span lang="EN-US">Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.</span></p>

Opinion mining framework using proposed RB-bayes model for text classication

<p><span lang="EN-US">Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.</span></p>

A Survey on Opinion Mining: From Stance to Product Aspect

With the prevalence of social media and online forum, opinion mining, aiming at analyzing and discovering the latent opinion in user-generated reviews on the Internet, has become a hot research topic. This survey focuses on two important subtasks in this field, stance detection and product aspect mining, both of which can be formalized as the problem of the triple (target, aspect, opinion) extraction. In this paper, we first introduce the general framework of opinion mining and describe the evaluation metrics. Then, the methodologies for stance detection on different sources, such as online forum and social media are discussed. After that, approaches for product aspect mining are categorized into three main groups which are corpus level aspect extraction, corpus level aspect, and opinion mining, and document level aspect and opinion mining based on the processing units and tasks. And then we discuss the challenges and possible solutions. Finally, we summarize the evolving trend of the reviewed methodologies and conclude the survey.

OPINION MININGFRAMEWORK BERBASISKAN LEKSIKAL

Opinion Mining and Sentiment Analysis can contribute in improving system performance tomeet the user’s needs. The text that represents the user's opinion may express the emotions oropinions implicitly or explicitly. The words that compose the sentence of opinion can have manydifferent meanings, related to the subject being discussed or the words that compose the sentence.This study, conducted a literature study on several researches that related to opinion mining, anddesigned an opinion mining framework based lexicon.

A survey on classification techniques for opinion mining and sentiment analysis

Opinion mining is considered as a subfield of natural language processing, information retrieval and text mining. Opinion mining is the process of extracting human thoughts and perceptions from unstructured texts, which with regard to the emergence of online social media and mass volume of users’ comments, has become to a useful, attractive and also challenging issue. There are varieties of researches with different trends and approaches in this area, but the lack of a comprehensive study to investigate them from all aspects is tangible. In this paper we represent a complete, multilateral and systematic review of opinion mining and sentiment analysis to classify available methods and compare their advantages and drawbacks, in order to have better understanding of available challenges and solutions to clarify the future direction. For this purpose, we present a proper framework of opinion mining accompanying with its steps and levels and then we completely monitor, classify, summarize and compare proposed techniques for aspect extraction, opinion classification, summary production and evaluation, based on the major validated scientific works. In order to have a better comparison, we also propose some factors in each category, which help to have a better understanding of advantages and disadvantages of different methods.

Aspect-based opinion mining framework using heuristic patterns

The aspect-based online opinions expressed by users on social media sites have become a popular source of information for consumers regarding their purchase decisions as well as for companies seeking opinions on their products. Therefore, it is important to develop aspect-based opinion mining applications with an emphasis on extracting and classifying the aspect-based opinions expressed by users about products in a given review. Previous studies have used a limited set of heuristic patterns for aspect extraction with both supervised (annotated-dataset-based) and unsupervised (lexical-resource-based) aspect-related sentiment classification algorithms. However, the present study proposes an integrated framework comprising of an extended set of heuristic patterns for aspect extraction, a hybrid sentiment classification module with the additional support of intensifiers and negations, and a summary generator. The performance evaluation of the proposed aspect-based opinion mining system using state-of-the-art methods shows that the proposed system outperforms the alternative methods in terms of better precision, recall and F-measure, since it achieves an average precision of 85%, an average recall of 73% and an average F-measure of 0.78. The comparative results indicate that the proposed technique provides more efficient results for the aspect-sentiment extraction, classification and summary generation of online product reviews.

Semantic Feature Based Arabic Opinion Mining Using Ontology

with the increase of opinionated reviews on the web, automatically analyzing and extracting knowledge from those reviews is very important. However, it is a challenging task to be done manually. Opinion mining is a text mining discipline that automatically performs such a task. Most researches done in this field were focused on English texts with very limited researches on Arabic language. This scarcity is because there are a lot of obstacles in Arabic. The aim of this paper is to develop a novel semantic feature-based opinion mining framework for Arabic reviews. This framework utilizes the semantic of ontologies and lexicons in the identification of opinion features and their polarity. Experiments showed that the proposed framework achieved a good level of performance compared with manually collected test data.

Editorial preface for the special issue “Advances in security, privacy and trust technologies”

With the fast development of Internet technologies, the information and communication systems are continuously facing new security, trust and privacy challenges. Indeed, the potentially invasive nature of the information has implications on privacy of the users and their online activity such as transactions. Likewise, the implicit reliance on technology to make decisions on one’s behalf makes essential to study new mechanisms for expressing and reasoning about trust. This special issue brings together research articles covering diverse aspects of security, privacy and trust in computer and networked-based information systems, including privacy concerns, tools to help secure information, methods for development and assessment of trustworthy systems, analysis of vulnerabilities and attacks, trends and new developments, secure operating systems and applications, security issues in wireless networks and pervasive systems, design and test strategies for secure and survivable systems, and cryptology, among others. The papers of the special issue are selected based on strict review results, where papers went through three rounds of review and revision. As a result, nine papers were selected and are arranged in the special issue as follows. Xiong et al. in the first paper ‘‘The finite volume element method for a parameter identification problem’’ use finite volume element method to solve a parameter identification problem of parabolic equations with over-specified data. The authors provide the numerical scheme of the unknown function and control parameters and obtain the error estimates of approximate solution. The proposed method is experimentally evaluated and compared with the exact solution to confirm the good accuracy of the presented scheme. The second paper by Chen et al. ‘‘An Opinion Mining Framework For Cantonese Reviews’’, analyze opinion mining for Cantonese language. The authors motivate the need to construct a particular lexical database for Cantonese and explore some Cantonese special written-tradition rules and incorporate them into the feature-based opinion summarization system framework. The experimental results show that the proposed framework significantly outperforms the traditional Mandarin sentiment analysis method using ICTCLAS. In the third paper ‘‘A Secure Remote Data Integrity Checking Cloud Storage System from Threshold Encryption’’, Yao et al. study the confidentiality and integrity issues in cloud storage system. The authors address the privacy issue of decentralized cloud storage system using threshold cryptography. The threshold encryption scheme is therefore integrated with a secure decentralized erasure code to form a secure cloud storage system. Zhang et al. in the fourth paper ‘‘How to build a faster private information retrieval protocol’’, propose a secure multi-bit homomorphic encryption scheme based on Learning With Errors over Rings (RLWE) assumption. The authors use canonical embedding to transform ring & Fatos Xhafa fatos@cs.upc.edu

Review Mining for Feature based Opinion Summarization and Visualization

The application and usage of opinion mining, especially for business intelligence, product recommendation, targeted marketing etc. have fascinated many research attentions around the globe. Various research efforts attempted to mine opinions from customer reviews at different levels of granularity, including word-, sentence-, and document-level. However, development of a fully automatic opinion mining and sentiment analysis system is still elusive. Though the development of opinion mining and sentiment analysis systems are getting momentum, most of them attempt to perform document-level sentiment analysis, classifying a review document as positive, negative, or neutral. Such document-level opinion mining approaches fail to provide insight about users sentiment on individual features of a product or service. Therefore, it seems to be a great help for both customers and manufacturers, if the reviews could be processed at a finer-grained level and presented in a summarized form through some visual means, highlighting individual features of a product and users sentiment expressed over them. In this paper, the design of a unified opinion mining and sentiment analysis framework is presented at the intersection of both machine learning and natural language processing approaches. Also, design of a novel feature-level review summarization scheme is proposed to visualize mined features, opinions and their polarity values in a comprehendible way.