Exploiting and Ranking Dominating Product Features through Communal Sentiments

The rapidly expanding e-commerce has facilitated consumers to purchase products online. Various brands and millions of products have been offered online. Varieties of customers’ reviews are available now days in internet. These reviews are important for the consumers as well as the merchants. Most of the reviews are disorganized so it generates difficulty for usefulness of information. In this paper we are proposing a product feature ranking framework, which will identify important features of products from online customer opinions, and aim to improve the usability of the different reviews. The important product features are recognized using two observations 1) the important features are mostly commented on by a large number of users 2) users reviews on the important features are greatly influence on the overall reviews on the product. We first identify product features by shallow dependency parser and determine customer’s reviews on these features via a sentiment classifier. Then we adopt develop a probabilistic feature ranking algorithm to conclude the importance of features by considering frequency and the influence of the influence of the users reviews given to each feature over their overall reviews. DOI: 10.17762/ijritcc2321-8169.15068

Atrial fibrillation detection using support vector machine and electrocardiographic descriptive statistics

Copyright © 2017 Inderscience Enterprises Ltd. This paper proposes a new technique for detecting atrial fibrillation (AF). The method employs electrocardiographic features and support vector machine (SVM). The features include descriptive statistics of electrocardiographic RR interval. The RR interval is the distance in time between two consecutive R-peaks of electrocardiogram. AF detections using SVM with different electrocardiographic features and different SVM free parameters are explored. Employing SVM with the optimal free parameters and all the proposed electrocardiographic features, we find an AF detection technique with a comparable performance. The best performance obtained by the technique is 98.47% and 97.84%, in terms of sensitivity and specificity

Opinion Mining of Movie Review using Hybrid Method of Support Vector Machine and Particle Swarm Optimization

Nowadays, online social media is online discourse where people contribute to create content, share it, bookmark it, and network at an impressive rate. The faster message and ease of use in social media today is Twitter. The messages on Twitter include reviews and opinions on certain topics such as movie, book, product, politic, and so on. Based on this condition, this research attempts to use the messages of twitter to review a movie by using opinion mining or sentiment analysis. Opinion mining refers to the application of natural language processing, computational linguistics, and text mining to identify or classify whether the movie is good or not based on message opinion. Support Vector Machine (SVM) is supervised learning methods that analyze data and recognize the patterns that are used for classification. This research concerns on binary classification which is classified into two classes. Those classes are positive and negative. The positive class shows good message opinion; otherwise the negative class shows the bad message opinion of certain movies. This justification is based on the accuracy level of SVM with the validation process uses 10-Fold cross validation and confusion matrix. The hybrid Partical Swarm Optimization (PSO) is used to improve the election of best parameter in order to solve the dual optimization problem. The result shows the improvement of accuracy level from 71.87% to 77%

Sentiment Analysis over Online Product Reviews: A Survey

Prior to the invention of the internet while purchasing any product people used to ask the opinions to his family, friends for particular product. but now a days as the swift increase of usage of the internet, more users are motivated to write their feelings about particulars in the form of comments on different sites like Facebook, twitter, online shopping sites, blogs, etc. this comments are nothing but the sentiments of the users this may be positive, negative or neutral. There are various techniques used for summarizing the customer comments like Data mining, Text clssification, Retrieval of informtaion, and summarizing the text. People tend to write their reviews over a product over different sites. Most of the reviews are critical to conclude so it generates difficulty for usefulness of information. If anyone want to know the impact of the particular post/product then it becomes difficult to read all the comments and to classify it. Sentiment analysis is the ongoing research field in the data mining, Sentiment analysis is also referred as opinion mining. This field mainly deals with classifying the sentiments among different types of comments that are written by various users. This paper is about to discuss different techniques, challenges and applications related to sentiment analysis

Opinion Mining Summarization and Automation Process: A Survey

In this modern age, the internet is a powerful source of information. Roughly, one-third of the world population spends a significant amount of their time and money on surfing the internet. In every field of life, people are gaining vast information from it such as learning, amusement, communication, shopping, etc. For this purpose, users tend to exploit websites and provide their remarks or views on any product, service, event, etc. based on their experience that might be useful for other users. In this manner, a huge amount of feedback in the form of textual data is composed of those webs, and this data can be explored, evaluated and controlled for the decision-making process. Opinion Mining (OM) is a type of Natural Language Processing (NLP) and extraction of the theme or idea from the user's opinions in the form of positive, negative and neutral comments. Therefore, researchers try to present information in the form of a summary that would be useful for different users. Hence, the research community has generated automatic summaries from the 1950s until now, and these automation processes are divided into two categories, which is abstractive and extractive methods. This paper presents an overview of the useful methods in OM and explains the idea about OM regarding summarization and its automation process

Sentiment Analysis of Assamese Text Reviews: Supervised Machine Learning Approach with Combined n-gram and TF-IDF Feature

Sentiment analysis (SA) is a challenging application of natural language processing (NLP) in various Indian languages. However, there is limited research on sentiment categorization in Assamese texts. This paper investigates sentiment categorization on Assamese textual data using a dataset created by translating Bengali resources into Assamese using Google Translator. The study employs multiple supervised ML methods, including Decision Tree, K-nearest neighbour, Multinomial Naive Bayes, Logistic Regression, and Support Vector Machine, combined with n-gram and Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction methods. The experimental results show that Multinomial Naive Bayes and Support Vector Machine have over 80% accuracy in analyzing sentiments in Assamese texts, while the Unigram model performs better than higher-order n-gram models in both datasets. The proposed model is shown to be an effective tool for sentiment classification in domain-independent Assamese text data

Sentiment Analysis and Opinion Mining within Social Networks using Konstanz Information Miner

Evaluations, opinions, and sentiments have become very obvious due to rapid emerging interest in ecommerce which is also a significant source of expression of opinions and analysis of sentiment. In this study, a general introduction on sentiment analysis, steps of sentiment analysis, sentiments analysis applications, sentiment analysis research challenges, techniques used for sentiment analysis, etc., were discussed in detail. With these details given, it is hoped that researchers will engage in opinion mining and sentiment analysis research to attain more successes correlated to these issues. The research is based on data input from web services and social networks, including an application that performs such actions. The main aspects of this study are to statistically test and evaluate the major social network websites: In this case Twitter, because it is has rich data source and easy within social networks tools. In this study, firstly a good understanding of sentiment analysis and opinion mining research based on recent trends in the field is provided. Secondly, various aspects of sentiment analysis are explained. Thirdly, various steps of sentiment analysis are introduced. Fourthly, various sentiment analysis, research challenges are discussed. Finally, various techniques used for sentiment analysis are explained and Konstanz Information Miner (KNIME) that can be used as sentiment analysis tool is introduced. For future work, recent machine learning techniques including big data platforms may be proposed for efficient solutions for opinion mining and sentiment analysi

PENINGKATAN OPTIMASI SENTIMEN DALAM PELAKSANAAN PROSES PEMILIHAN PRESIDEN BERDASARKAN OPINI PUBLIK DENGAN MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN PARICLE SWARM OPTIMIZATION

Abstract- The development of increasingly advanced IT in the process of presidential elections. When the Presidential election of 2014 yesterday has a lot of people use the phrase does not educate inappropriate to be delivered among the public. Pros and cons indeed occur among people are so warm that they pour on the internet. This happens because when getting warm diperbincangan 2014 presidential election yesterday happened pengkubu-kubuan two candidates. Society can not adjust the development of IT process well. Naive Bayes is widely used for classification problems in data mining and machine learning for its simplicity and accuracy of classification impressive. Naive Bayes classifier has been shown to be very effective to solve the problem of large scale for text categorization with high accuracy. In addition to having many capabilities mentioned above, however this method has a drawback in the assumptions that are difficult to fulfill, namely the independence of the feature. Particle Swarm Optimization (PSO) is an evolutionary computation technique which is able to produce globally optimal solution in the search space through the interaction of individuals in a swarm of particles. PSO is widely used to solve optimization problems as well as the feature selection. Accuracy is generated on Naive Bayes algorithm amounted to 63.85% and AUC by 0523, while Naive Bayes and Particle Swarm Optimmization with an accuracy of 71.15% and the AUC of 0.600. It can be concluded that the application of optimization can improve the accuracy of 63.85% to 71.15%. Naive Bayes Model and Particle Swarm Optimization can provide solutions to the problems of classification review of public opinion news of the election in order to more accurately and optimally. Keywords:Public Opinion, Classification, Naive Bayes, Particle Swarm Optimization, Text Mining