scholarly journals Naïve Multi-label Classification of YouTube Comments Using Comparative Opinion Mining

2016 ◽  
Vol 82 ◽  
pp. 57-64 ◽  
Author(s):  
Asad Ullah Rafiq Khan ◽  
Madiha Khan ◽  
Mohammad Badruddin Khan
Keyword(s):  
Opinion Mining ◽  
Comparative Opinion Mining

Index of suicide risk in Mexico using Twitter

2019 ◽  
pp. 1-13
Author(s):  
Luz Judith Rodríguez-Esparza ◽  
Diana Barraza-Barraza ◽  
Jesús Salazar-Ibarra ◽  
Rafael Gerardo Vargas-Pasaye
Keyword(s):  
Social Networks ◽  
Analytic Hierarchy Process ◽  
Suicide Risk ◽  
Opinion Mining ◽  
Real Data ◽  
Analytic Hierarchy ◽  
Specialized Care ◽  
The Analytic Hierarchy Process ◽  
Hierarchy Process

Objectives: To identify early suicide risk signs on depressive subjects, so that specialized care can be provided. Various studies have focused on studying expressions on social networks, where users pour their emotions, to determine if they show signs of depression or not. However, they have neglected the quantification of the risk of committing suicide. Therefore, this article proposes a new index for identifying suicide risk in Mexico. Methodology: The proposal index is constructed through opinion mining using Twitter and the Analytic Hierarchy Process. Contribution: Using R statistical package, a study is presented considering real data, making a classification of people according to the obtained index and using information from psychologists. The proposed methodology represents an innovative prevention alternative for suicide.

Polarity Classification of Arabic Sentiments

2016 ◽  
Vol 11 (3) ◽  
pp. 32-49 ◽  
Author(s):  
Mohammed N. Al-Kabi ◽  
Heider A. Wahsheh ◽  
Izzat M. Alsmadi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Operating Characteristic ◽  
Opinion Mining ◽  
Online Social Network ◽  
The Social ◽  
Polarity Classification ◽  
Arabic Sentiment Analysis ◽  
Modern Standard

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

Classification of YouTube Data based on Opinion Mining

2021 ◽  
Author(s):  
Ritika Jain ◽  
Riya Garg ◽  
Muskan Verma ◽  
Anuradha Taluja
Keyword(s):  
Opinion Mining

scholarly journals Classification of Fake Product Ratings Using a Timeline Based Approach

2017 ◽  
Vol 3 (2) ◽  
pp. 12 ◽  
Author(s):  
Neha Thomas ◽  
Susan Elias
Keyword(s):  
Language Processing ◽  
Opinion Mining ◽  
Optimal Point ◽  
Linear Classifiers ◽  
Wide Range ◽  
Text Content ◽  
Classification Tool ◽  
Fake Reviews ◽  
Product Ratings

 Abstract— Detection of fake review and reviewers is currently a challenging problem in cyber space. It is challenging primarily due to the dynamic nature of the methodology used to fake the review. There are several aspects to be considered when analyzing reviews to classify them effective into genuine and fake. Sentiment analysis, opinion mining and intend mining are fields of research that try to accomplish the goal through Natural Language Processing of the text content of the review.  In this paper, an approach that uses the review ratings evaluated along a timeline is presented. An Amazon dataset comprising of ratings indicated for a wide range of products was used for the analysis presented here. The analysis of the ratings was carried out for an electronic product over a period of six years.  The computed average rating helps to identify linear classifiers that define solution boundaries within the dataspace. This enables a product specific classification of review ratings and suitable recommendations can also be generated automatically. The paper explains a methodology to evaluate the average product ratings over time and presents the research outcomes using a novel classification tool. The proposed approach helps to determine the optimal point to distinguish between fake and genuine ratings for each product.    Index Terms: Fake reviews, Fake Ratings, Product Ratings, Online Shopping, Amazon Dataset.

scholarly journals Classification of Opinion Mining Techniques

2012 ◽  
Vol 56 (13) ◽  
pp. 1-6 ◽  
Author(s):  
Nidhi Mishra ◽  
C. K. Jha
Keyword(s):  
Opinion Mining

A gradient boosted decision tree-based sentiment classification of twitter data

2020 ◽  
Vol 18 (04) ◽  
pp. 2050027
Author(s):  
S. Neelakandan ◽  
D. Paulraj
Keyword(s):  
Decision Tree ◽  
Opinion Mining ◽  
Research Topic ◽  
Sentiment Classification ◽  
Decision Tree Classifier ◽  
Twitter Data ◽  
Tree Classifier ◽  
Boosted Decision Tree ◽  
Text Sentiment Analysis

People communicate their views, arguments and emotions about their everyday life on social media (SM) platforms (e.g. Twitter and Facebook). Twitter stands as an international micro-blogging service that features a brief message called tweets. Freestyle writing, incorrect grammar, typographical errors and abbreviations are some noises that occur in the text. Sentiment analysis (SA) centered on a tweet posted by the user, and also opinion mining (OM) of the customers review is another famous research topic. The texts are gathered from users’ tweets by means of OM and automatic-SA centered on ternary classifications, namely positive, neutral and negative. It is very challenging for the researchers to ascertain sentiments as a result of its limited size, misspells, unstructured nature, abbreviations and slangs for Twitter data. This paper, with the aid of the Gradient Boosted Decision Tree classifier (GBDT), proposes an efficient SA and Sentiment Classification (SC) of Twitter data. Initially, the twitter data undergoes pre-processing. Next, the pre-processed data is processed using HDFS MapReduce. Now, the features are extracted from the processed data, and then efficient features are selected using the Improved Elephant Herd Optimization (I-EHO) technique. Now, score values are calculated for each of those chosen features and given to the classifier. At last, the GBDT classifier classifies the data as negative, positive, or neutral. Experiential results are analyzed and contrasted with the other conventional techniques to show the highest performance of the proposed method.

scholarly journals Opinion Mining on Culinary Food Customer Satisfaction Using Naïve Bayes Based-on Hybrid Feature Selection

2019 ◽  
Vol 15 (1) ◽  
pp. 468 ◽  
Author(s):  
Oman Somantri ◽  
Dyah Apriliani
Keyword(s):  
Feature Selection ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Information Gain ◽  
Naïve Bayes ◽  
Classification Model ◽  
Consumer Ratings ◽  
Bayes Algorithm ◽  
Restaurant Owners

<p>Conducting an assessment of consumer sentiments taken from social media in assessing a culinary food gives useful information for everyone who wants to get this information especially for migrants and tourists, in th other hand that information is very valuable for food stall and restaurant owners as information in improvinf food quality. Overcoming this problem, a sentiment analysis classification model using naïve bayes algorithm (NB) was applied to get this information. This problem occurs is the level of accuracy of classification of consumer ratings of culinary food is still not optimal because the weight of values in the data preprocessing process are not optimal. In this paper proposed a hybrid feature selection models to overcome the problems in the process of selecting the feature attributes that have not been optimal by using a combination of information gain (IG) and genetic algorithm (GA) algorithms. The result of this research showed that after the experiment and compared to using others algorithms produce the best of the level occuracy is 93%.</p>

scholarly journals Comparative opinion mining: A review

2016 ◽  
Vol 68 (4) ◽  
pp. 811-829 ◽  
Author(s):  
Kasturi Dewi Varathan ◽  
Anastasia Giachanou ◽  
Fabio Crestani
Keyword(s):  
Opinion Mining ◽  
Comparative Opinion Mining
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