scholarly journals Sentence Level Domain Independent Opinion and Targets Identification in Unstructured Reviews

Computers ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 70
Author(s):  
Kahirullah Khan ◽  
Wahab Khan
Keyword(s):  
Language Processing ◽  
Opinion Mining ◽  
Second Phase ◽  
Opinion Extraction ◽  
Connection Machine ◽  
Sentence Level ◽  
Two Phases ◽  
Independent Features ◽  
Processing Techniques ◽  
Domain Independent

User reviews, blogs, and social media data are widely used for various types of decision-making. In this connection, Machine Learning and Natural Language Processing techniques are employed to automate the process of opinion extraction and summarization. We have studied different techniques of opinion mining and found that the extraction of opinion target and opinion words and the relation identification between them are the main tasks of state-of-the-art techniques. Furthermore, domain-independent features extraction is still a challenging task, since it is costly to manually create an extensive list of features for every domain. In this study, we tested different syntactic patterns and semantic rules for the identification of evaluative expressions containing relevant target features and opinion. We have proposed a domain-independent framework that consists of two phases. First, we extract Best Fit Examples (BFE) consisting of short sentences and candidate phrases and in the second phase, pruning is employed to filter the candidate opinion targets and opinion words. The results of the proposed model are significant.

scholarly journals Analysis of Machine Learning Algorithms for Opinion Mining in Different Domains

2018 ◽  
Vol 1 (1) ◽  
pp. 224-234 ◽  
Author(s):  
Donia Gamal ◽  
Marco Alfonse ◽  
El-Sayed M. El-Horbaty ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Second Phase ◽  
Adaptive Boosting ◽  
Two Phases ◽  
Textual Content

Sentiment classification (SC) is a reference to the task of sentiment analysis (SA), which is a subfield of natural language processing (NLP) and is used to decide whether textual content implies a positive or negative review. This research focuses on the various machine learning (ML) algorithms which are utilized in the analyzation of sentiments and in the mining of reviews in different datasets. Overall, an SC task consists of two phases. The first phase deals with feature extraction (FE). Three different FE algorithms are applied in this research. The second phase covers the classification of the reviews by using various ML algorithms. These are Naïve Bayes (NB), Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Passive Aggressive (PA), Maximum Entropy (ME), Adaptive Boosting (AdaBoost), Multinomial NB (MNB), Bernoulli NB (BNB), Ridge Regression (RR) and Logistic Regression (LR). The performance of PA with a unigram is the best among other algorithms for all used datasets (IMDB, Cornell Movies, Amazon and Twitter) and provides values that range from 87% to 99.96% for all evaluation metrics.

An Opinion Mining Approach for Drug Reviews in Spanish

2021 ◽  
pp. 445-480
Author(s):  
Karina Castro-Pérez ◽  
José Luis Sánchez-Cervantes ◽  
María del Pilar Salas-Zárate ◽  
Maritza Bustos-López ◽  
Lisbeth Rodríguez-Mazahua
Keyword(s):  
Language Processing ◽  
Opinion Mining ◽  
Adverse Drug Events ◽  
Hybrid Approach ◽  
Unstructured Data ◽  
Degenerative Diseases ◽  
Analysis System ◽  
Essential Contribution ◽  
Processing Techniques ◽  
Manual Review

In recent years, the application of opinion mining has increased as a boom and growth of social media and blogs on the web, and these sources generate a large volume of unstructured data; therefore, a manual review is not feasible. For this reason, it has become necessary to apply web scraping and opinion mining techniques, two primary processes that help to obtain and summarize the data. Opinion mining, among its various areas of application, stands out for its essential contribution in the context of healthcare, especially for pharmacovigilance, because it allows finding adverse drug events omitted by the pharmaceutical companies. This chapter proposes a hybrid approach that uses semantics and machine learning for an opinion mining-analysis system by applying natural-language-processing techniques for the detection of drug polarity for chronic-degenerative diseases, available in blogs and specialized websites in the Spanish language.

Opinion Mining for Instructor Evaluations at the Autonomous University of Ciudad Juarez

2021 ◽  
pp. 427-444
Author(s):  
Rafael Jiménez ◽  
Vicente García ◽  
Abraham López ◽  
Alejandra Mendoza Carreón ◽  
Alan Ponce
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Opinion Mining ◽  
Experimental Results ◽  
Ciudad Juarez ◽  
Teaching Process ◽  
Instructor Evaluation ◽  
Negative Comments ◽  
Processing Techniques

The Autonomous University of Ciudad Juárez performs an instructor evaluation each semester to find strengths, weaknesses, and areas of opportunity during the teaching process. In this chapter, the authors show how opinion mining can be useful for labeling student comments as positives and negatives. For this purpose, a database was created using real opinions obtained from five professors of the UACJ over the last four years, covering a total of 20 subjects. Natural language processing techniques were used on the database to normalize its data. Experimental results using 1-NN and Bagging classifiers shows that it is possible to automatically label positive and negative comments with an accuracy of 80.13%.

Opinion Mining and Information Retrieval

2011 ◽  
pp. 640-652
Author(s):  
Shishir K. Shandilya ◽  
Suresh Jain
Keyword(s):  
Machine Learning ◽  
Natural Language ◽  
Language Processing ◽  
Ambient Intelligence ◽  
Opinion Mining ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Web Documents ◽  
Opinion Extraction ◽  
Traditional Natural

The explosive increase in Internet usage has attracted technologies for automatically mining the user-generated contents (UGC) from Web documents. These UGC-rich resources have raised new opportunities and challenges to carry out the opinion extraction and mining tasks for opinion summaries. The technology of opinion extraction allows users to retrieve and analyze people’s opinions scattered over Web documents. Opinion mining is a process which is concerned with the opinions generated by the consumers about the product. Opinion Mining aims at understanding, extraction and classification of opinions scattered in unstructured text of online resources. The search engines performs well when one wants to know about any product before purchase, but the filtering and analysis of search results often complex and time-consuming. This generated the need of intelligent technologies which could process these unstructured online text documents through automatic classification, concept recognition, text summarization, etc. These tools are based on traditional natural language techniques, statistical analysis, and machine learning techniques. Automatic knowledge extraction over large text collections like Internet has been a challenging task due to many constraints such as needs of large annotated training data, requirement of extensive manual processing of data, and huge amount of domain-specific terms. Ambient Intelligence (AmI) in wed-enabled technologies supports and promotes the intelligent e-commerce services to enable the provision of personalized, self-configurable, and intuitive applications for facilitating UGC knowledge for buying confidence. In this chapter, we will discuss various approaches of Opinion Mining which combines Ambient Intelligence, Natural Language Processing and Machine Learning methods based on textual and grammatical clues.

Analysis on Opinion Words Extraction in Electronic Product Reviews

2019 ◽  
Vol 10 (1) ◽  
pp. 47-61
Author(s):  
Sint Sint Aung
Keyword(s):  
Language Processing ◽  
Opinion Mining ◽  
Research Area ◽  
Sentiment Classification ◽  
Training Data ◽  
Product Reviews ◽  
Research Areas ◽  
Product Features ◽  
Opinion Extraction ◽  
Training Examples

Online user reviews are increasingly becoming important for measuring the quality of different products and services. Sentiment classification or opinion mining involves studying and building a system that collects data from online and examines the opinions. Sentiment classification is also defined as opinion extraction as the computational research area of subjective information towards different products. Opinion mining or sentiment classification has attracted in many research areas because of its usefulness in natural language processing and other area of applications. Extracting opinion words and product features are also important tasks in opinion mining. In this work an unsupervised approach was proposed to extract opinions and product features without training examples. To obtain the dependency relation between the product aspects and opinions, this work used StanfordCoreNLP dependency parser. From these relations, rules are predified to extract product and opinions. The main advantage of this approach is that there is no need for training data and it has domain independence. Acoording to the experimental results, the modified algorithm gets better results than the double propagation algorithm.

A Classification Framework on Opinion Mining for Effective Recommendation Systems

2018 ◽  
pp. 980-994
Author(s):  
Mahima Goyal ◽  
Vishal Bhatnagar
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Classification Framework ◽  
Sentence Level ◽  
The Social ◽  
Social Media Platforms ◽  
Mining Methods ◽  
Document Level

With the recent trend of expressing opinions on the social media platforms like Twitter, Blogs, Reviews etc., a large amount of data is available for the analysis in the form of opinion mining. This analysis plays pivotal role in providing recommendation for ecommerce products, services and social networks, forecasting market movements and competition among businesses, etc. The authors present a literature review about the different techniques and applications of this field. The primary techniques can be classified into Data Mining methods, Natural Language Processing (NLP) and Machine learning algorithms. A classification framework is designed to depict the three levels of opinion mining –document level, Sentence Level and Aspect Level along with the methods involved in it. A system can be recommended on the basis of content based and collaborative filtering

scholarly journals Forecasting Nonlinear Systems with LSTM: Analysis and Comparison with EKF

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1805
Author(s):  
Juan Pedro Llerena Caña ◽  
Jesús García Herrero ◽  
José Manuel Molina López
Keyword(s):  
Language Processing ◽  
Network Architecture ◽  
Engineering Process ◽  
Neural Network Architecture ◽  
Previous Hypothesis ◽  
Initial Hypothesis ◽  
Improved Performance ◽  
Two Phases ◽  
Estimation And Filtering ◽  
Processing Techniques

Certain difficulties in path forecasting and filtering problems are based in the initial hypothesis of estimation and filtering techniques. Common hypotheses include that the system can be modeled as linear, Markovian, Gaussian, or all at one time. Although, in many cases, there are strategies to tackle problems with approaches that show very good results, the associated engineering process can become highly complex, requiring a great deal of time or even becoming unapproachable. To have tools to tackle complex problems without starting from a previous hypothesis but to continue to solve classic challenges and sharpen the implementation of estimation and filtering systems is of high scientific interest. This paper addresses the forecast–filter problem from deep learning paradigms with a neural network architecture inspired by natural language processing techniques and data structure. Unlike Kalman, this proposal performs the process of prediction and filtering in the same phase, while Kalman requires two phases. We propose three different study cases of incremental conceptual difficulty. The experimentation is divided into five parts: the standardization effect in raw data, proposal validation, filtering, loss of measurements (forecasting), and, finally, robustness. The results are compared with a Kalman filter, showing that the proposal is comparable in terms of the error within the linear case, with improved performance when facing non-linear systems.

A review of natural language processing techniques for opinion mining systems

2017 ◽  
Vol 36 ◽  
pp. 10-25 ◽  
Author(s):  
Shiliang Sun ◽  
Chen Luo ◽  
Junyu Chen
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Opinion Mining ◽  
Processing Techniques

scholarly journals Sentiment analysis: a challenge

2018 ◽  
Vol 7 (2.27) ◽  
pp. 291 ◽  
Author(s):  
Ekta Gupta ◽  
Ashok Kumar ◽  
Manish Kumar
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Accurate Result ◽  
Product Reviews ◽  
General Process ◽  
Sentence Level ◽  
Complete Procedure ◽  
Future Work ◽  
Online Buying

Sentiment analysis or judgment/thoughts mining is one of the major jobs of NLP (Natural Language Processing). Sentiment analysis has acquired much awareness in recent years. In this paper, our focus is to approach the problem of sentiment polarity assortment, which is one of the elementary problems of sentiment analysis. A general process for sentiment polarity assortment is considered with complete procedure explanation. Data used in this research are online buying product reviews collected from the shopping platform Amazon.com. Experiments for both sentence-level assortment and review-level assortment are executed with guarantee outcomes. Sentiment analysis will help to enhance the business with its performance of giving accurate result .In the end; we also give awareness into our future work on sentiment analysis. From last decade there is no such work has done on sentiment analysis to improve the product quality on the basis of what the customer needs and sometimes it is introduce as opinion mining while the importance in this case is on extraction.  

scholarly journals Speech and Opinion Recognition from a Conversation

2020 ◽  
Vol 9 (4) ◽  
pp. 1189-1193
Keyword(s):  
Machine Learning ◽  
Computational Linguistics ◽  
Language Processing ◽  
Opinion Mining ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Accuracy And Precision ◽  
The Subject ◽  
Processing Techniques

Speech Recognition is an interdisciplinary technique used to convert spoken language into text. It is a sub domain of computational linguistics and can be implemented using Machine Learning and Deep Learning Algorithms. Opinion Mining or Sentiment Analysis is a process which enables identifying opinions expressed by an author in a piece of text computationally. This opinion refers to the polarity of the expressed opinion, i.e. positive or negative. Through this research work, we aim to combine these two natural language processing techniques and devise a system that can take speech as the input and determine the sentiment behind the speakers’ words. The subject of the speech input may vary but the end goal is to recognize whether the attitude of the speaker towards the subject was positive or negative. The input will be converted to text and this text will then be classified using several different machine learning techniques. These include Naïve Bayes’ Classifier, Support Vector Machine, Logistic Regression and Decision Trees. After classification, the results for the three classifiers will be predicted and compared. Future scope of the project includes creating an ensemble of these classifiers to get better accuracy and precision of determining the sentiment of the speaker

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