Automated Opinion Mining Research Articles

Understanding Large-Scale Network Effects in Detecting Review Spammers

Opinion spam detection is a challenge for online review systems and social forum operators. Opinion spamming costs businesses and people money since it deceives customers as well as automated opinion mining and sentiment analysis systems by bestowing undeserved positive opinions on target firms and/or bestowing fake negative opinions on others. One popular detection approach is to model a review system as a network of users, products, and reviews, for example using review graph models. In this article, we study the effects of network scale on network-based review spammer detection models, specifically on the trust model and the SpammerRank model. We then evaluate both network models using two large publicly available review datasets, namely: the Amazon dataset (containing 6 million reviews by more than 2 million reviewers) and the UCSD dataset (containing over 82 million reviews by 21 million reviewers). It has been observed thatSpammerRank model provides a better scaling time for applications requiring reviewer indicators and in case of trust model distributions are flattening out indicating variance of reviews with respect to spamming. Detailed observations on the scaling effects of these models are reported in the result section.

APPLYING OPINION MINING ALGORITHMS TO ANALYZE THE USER SENTIMENTS FROM USER REVIEWS TO RATE THE RESTAURANTS IN SULTANATE OF OMAN

Humans now spend the bulk of their time on the Internet, thanks to the huge rise of social media and other applications. Organizations and individuals have a shared platform to express their thoughts and ideas on any entity in the globe. Every website has a massive amount of information about products and services. Through social media tools, we, as humans, may remark on any subject or incident. Most of the time, these public opinions are considered by organizations and individuals to better corporate operations and strategies, or in the decision-making process. Although these user opinions are practical in decision-making processes, manually analyzing and summarizing them is a difficult effort. It is a lengthy process. As a result, having automated opinion mining techniques to analyze user sentiments is critical. Since the beginning of social media, sentiment analysis has been a hot research area. Many studies have been conducted in various fields. The tourism sector is highlighted as one of the important topics for diversification in Oman Vision 2040. This study suggests an analysis of user reviews of Omani restaurants, categorizing them as positive or bad, and ranking the restaurants according to the reviews. The reviews are analyzed using opinion mining methods

Design of Automated Opinion Mining Model Using Optimized Fuzzy Neural Network

Sentiment analysis or Opinion Mining (OM) has gained significant interest among research communities and entrepreneurs in the recent years. Likewise, Machine Learning (ML) approaches is one of the interesting research domains that are highly helpful and are increasingly applied in several business domains. In this background, the current research paper focuses on the design of automated opinion mining model using Deer Hunting Optimization Algorithm (DHOA) with Fuzzy Neural Network (FNN) abbreviated as DHOA-FNN model. The proposed DHOA-FNN technique involves four different stages namely, preprocessing, feature extraction, classification, and parameter tuning. In addition to the above, the proposed DHOA-FNN model has two stages of feature extraction namely, Glove and N-gram approach. Moreover, FNN model is utilized as a classification model whereas GTOA is used for the optimization of parameters. The novelty of current work is that the GTOA is designed to tune the parameters of FNN model. An extensive range of simulations was carried out on the benchmark dataset and the results were examined under diverse measures. The experimental results highlighted the promising performance of DHOA-FNN model over recent state-of-the-art techniques with a maximum accuracy of 0.9928.

Opinion Mining Analysis of Twitter Users of Particular Topic

Opinion mining consists of many different fields like natural language processing, text mining, decision making and linguistics. Opinion mining is a type of natural language processing for tracking the mood of the public about a particular product. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. This project addresses the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.

Optimizing service offerings using asymmetric impact-sentiment-performance analysis

• An integrated attribute-satisfaction model is introduced to optimize service offerings based on opinion mining user-generated content. • The attribute impact-asymmetry, impact-range, and sentiment-performances of service attributes are jointly visualized in a 3D grid. • Novel understanding of customers’ service assessments is gained, leading to attribute prioritization and specific recommendations for optimizing future service offerings. Researchers refer to various theories to investigate the distinct relationships between importance, performance, and the (a)symmetric impact of service attributes on customer satisfaction (CS). However, a fully integrated model that would allow practitioners to automatically execute analyses to optimize their service offerings in a competitive landscape is missing. Previous studies widely rely on importance/performance ratings of predefined service attributes retrieved from closed-ended questionnaires, which can hardly capture the competitive landscape from the customers’ perspective. This paper introduces a novel asymmetric impact-sentiment-performance analysis (AISPA) to address these gaps by performing automated opinion mining on online reviews. Customers’ evaluations of three hotel chains serve as an example application. The impact-asymmetry of the hotel service attributes on CS, the attribute impact and performance are jointly visualized in a 3D grid. An elaborate understanding of service assessments is gained, leading to attribute prioritization and specific recommendations for optimizing future offerings.

Opinion Mining and Sentiment Analysis on Big Data

<p>Opinion mining is extract subjective information from text data using tools such as NLP, text analysis etc. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product.In this project the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in terms of positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.</p>

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.

Domain-Specific Aspect-Sentiment Pair Extraction Using Rules and Compound Noun Lexicon for Customer Reviews

Online reviews are an important source of opinion to measure products’ quality. Hence, automated opinion mining is used to extract important features (aspect) and related comments (sentiment). Extraction of correct aspect-sentiment pairs is critical for overall outcome of opinion mining; however, current works still have limitations in terms of identifying special compound noun and parent-child relationship aspects in the extraction process. To address these problems, an aspect-sentiment pair extraction using the rules and compound noun lexicon (ASPERC) model is proposed. The model consists of three main phases, such as compound noun lexicon generation, aspect-sentiment pair rule generation, and aspect-sentiment pair extraction. The combined approach of rules generated from training sentences and domain specific compound noun lexicon enable extraction of more aspects by firstly identifying special compound noun and parent-child aspects, which eventually contribute to more aspect-sentiment pair extraction. The experiment is conducted with the SemEval 2014 dataset to compare proposed and baseline models. Both ASPERC and its variant, ASPER, result higher in recall (28.58% and 22.55% each) compared to baseline and satisfactorily extract more aspect sentiment pairs. Lastly, the reasonable outcome of ASPER indicates applicability of rules to various domains.

Applying data mining and machine learning techniques for sentiment shifter identification

Sentiment shifters, as a set of words and expressions that can affect text polarity, play a fundamental role in opinion mining. However, the limited ability of current automated opinion mining systems in handling shifters is a major challenge. This paper presents three novel and efficient methods for identifying sentiment shifters in reviews in order to improve the overall accuracy of opinion mining systems: two data mining based algorithms and a machine learning based algorithm. The data mining algorithms do not need shifter tagged datasets. They use weighted association rule mining (WARM) for finding frequent patterns representing sentiment shifters from a domain-specific and a general corpus. These patterns include different kinds of shifter words such as shifter verbs and quantifiers and are able to handle both local and long-distance shifters. The items in WARM for the two designed methods are in the form of dependency relations and SRL arguments of sentences, respectively. Secondly, we implemented a supervised machine learning system based on semantic features of sentences for shifter identification and polarity classification. This method obviously needs shifter tagged dataset for shifter identification. We tested our proposed algorithms on polarity classification task for 2 domains: a specific domain (drug reviews) and a general domain. Experiments demonstrate that (1) the extracted shifters improve the performance of the polarity classification, (2) the proposed data mining methods outperform other implemented methods in shifter identification, and (3) the proposed semantic based machine learning method has the best efficiency among all implemented methods in polarity classification.

Review of Classifiers for Automated Opinion Mining

Review of Classifiers for Automated Opinion Mining