scholarly journals Semi-Supervised Target-Dependent Sentiment Classification for Micro-Blogs

2019 ◽  
Vol 19 (01) ◽  
pp. e06
Author(s):  
Shadi I. Abudalfa ◽  
Moataz A. Ahmed
Keyword(s):  
Social Media ◽  
Supervised Learning ◽  
Classification Accuracy ◽  
Opinion Mining ◽  
Semisupervised Learning ◽  
Sentiment Classification ◽  
Short Text ◽  
Learning Techniques ◽  
The Social ◽  
Learning Technique

The wealth of opinions available in the social media motivated researchers to develop automatic opinion detection tools. Many such tools are currently available online for opinion mining in short text, known as micro-blogs, but their efficacies are still limited. Current tools focus on detecting sentiment polarity expressed in a micro-blog regardless of the topic (target) discussed. Little improved approaches have been proposed to detect sentiment towards a specific target, referred to as target-dependent sentiment classification. Our literature review has shown that all these target-dependent approaches use supervised learning techniques. Such techniques need a huge amount of labeled data for increasing classification accuracy. However, preparing labeled data from social media needs a lot of efforts. In this work, we address this issue by employing semisupervised learning techniques that have not been used before with target-dependent sentiment classification. To the best of our knowledge, our work is the first research that employs semisupervised learning techniques in this direction. Semi-supervised learning techniques have been known in the literature to improve classification accuracy in comparison with supervised learning techniques; however, they use same number of labeled samples plus many unlabelled ones. In this work, we propose a new semi-supervised learning technique that uses less number of labeled microblogs than that used with supervised learning techniques. Experiment results have shown that the proposed technique provides competitive accuracy.

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.

scholarly journals Identifying Botnet on IoT by Using Supervised Learning Techniques

2019 ◽  
Vol 12 (4) ◽  
pp. 185-193
Author(s):  
Amirhossein Rezaei
Keyword(s):  
Supervised Learning ◽  
Gradient Boosting ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Malicious Software ◽  
Learning Techniques ◽  
Security Challenges ◽  
Learning Technique ◽  
Security Challenge ◽  
The Moment

The security challenge on IoT (Internet of Things) is one of the hottest and most pertinent topics at the moment especially the several security challenges. The Botnet is one of the security challenges that most impact for several purposes. The network of private computers infected by malicious software and controlled as a group without the knowledge of owners and each of them running one or more bots is called Botnets. Normally, it is used for sending spam, stealing data, and performing DDoS attacks. One of the techniques that been used for detecting the Botnet is the Supervised Learning method. This study will examine several Supervised Learning methods such as; Linear Regression, Logistic Regression, Decision Tree, Naive Bayes, k- Nearest Neighbors, Random Forest, Gradient Boosting Machines, and Support Vector Machine for identifying the Botnet in IoT with the aim of finding which Supervised Learning technique can achieve the highest accuracy and fastest detection as well as with minimizing the dependent variable.

scholarly journals Application of Naïve Bayes Algorithm in Sentiment Analysis of Filipino, English and Taglish Facebook Comments

2020 ◽  
Vol 4 (5) ◽  
pp. 73-77
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Product Reviews ◽  
Documentary Data ◽  
The Social ◽  
Bayes Algorithm

The World Wide Web has boosted its content for the past years, it has a vast amount of multimedia resources that continuously grow specifically in documentary data. One of the major contributors of documentary contents can be evidently found on the social media called Facebook. People or netizens on Facebook are actively sharing their opinion about a certain topic or posts that can be related to them or not. With the huge amount of accessible documentary data that are seen on the so-called social media, there are research trends that can be made by the researchers in the field of opinion mining. A netizen’s comment on a particular post can either be a negative or a positive one. This study will discuss the opinion or comment of a netizen whether it is positive or negative or how she/he feels about a specific topic posted on Facebook; this is can be measured by the use of Sentiment Analysis. The combination of the Natural Language Processing and the analytics in textual form is also known as Sentiment Analysis that is use to the extraction of data in a useful manner. This study will be based on the product reviews of Filipinos in Filipino, English and Taglish (mixed Filipino and English) languages. To categorize a comment effectively, the Naïve Bayes Algorithm was implemented to the developed web system.

scholarly journals Improved semi-supervised learning technique for automatic detection of South African abusive language on Twitter

2020 ◽  
Vol 32 (2) ◽  
Author(s):  
Oluwafemi Oriola ◽  
Eduan Kotzé
Keyword(s):  
Logistic Regression ◽  
Supervised Learning ◽  
South African ◽  
Learning Curves ◽  
Training Data ◽  
Support Vector ◽  
Learning Techniques ◽  
Learning Technique ◽  
Unlabelled Data ◽  
Language Detection

Semi-supervised learning is a potential solution for improving training data in low-resourced abusive language detection contexts such as South African abusive language detection on Twitter. However, the existing semi-supervised learning methods have been skewed towards small amounts of labelled data, with small feature space. This paper, therefore, presents a semi-supervised learning technique that improves the distribution of training data by assigning labels to unlabelled data based on the majority voting over different feature sets of labelled and unlabelled data clusters. The technique is applied to South African English corpora consisting of labelled and unlabelled abusive tweets. The proposed technique is compared with state-of-the-art self-learning and active learning techniques based on syntactic and semantic features. The performance of these techniques with Logistic Regression, Support Vector Machine and Neural Networks are evaluated. The proposed technique, with accuracy and F1-score of 0.97 and 0.95, respectively, outperforms existing semi-supervised learning techniques. The learning curves show that the training data was used more efficiently by the proposed technique compared to existing techniques. Overall, n-gram syntactic features with a Logistic Regression classifier records the highest performance. The paper concludes that the proposed semi-supervised learning technique effectively detected implicit and explicit South African abusive language on Twitter.

scholarly journals The Opining Mining of Expatriate Adjustment and Significant Role of Social Media

2019 ◽  
Vol 6 (1) ◽  
pp. 42
Author(s):  
Benson Nwaorgu
Keyword(s):  
Social Media ◽  
Significant Role ◽  
Opinion Mining ◽  
Online Communication ◽  
Research Paper ◽  
Cross Cultural ◽  
Cultural Environment ◽  
Research Issues ◽  
Expatriate Adjustment ◽  
The Social

In this research paper, the researcher focused on the opining mining of expatriate adjustment and the significant roleof social media in real life. It is also one of the significant research issues for the person who are having citizenshipof one country and living in another country with a cross-cultural environment. The researcher would identify thefactors of a cross-cultural environment which are suitable and significant for the expatriate adjustment with the helpof social media. The social media is having of its own significant role in adjusting expatriate with respect to opinionmining. The opinion mining is one of the processes of seeking innovation of ideas and thought on the basis ofexisting or currently available in the real world. The social media is playing one of the biggest enablers for expatriatein a cross-cultural environment. The objective of this research paper is to identify the factors which are significantwith respect to opinion mining and social media in a cross-cultural environment. The researcher emphasized that thereason for online communication with respect to social media that flows are concept and opinion which thus play asignificant role in the cohesion and transformation of society. Its minimal concern on the emergence of opinions,widespread, and get benefits. The researcher used the social media for the adjustment with respect to expatriate’sadjustment.

scholarly journals FEATURE BASED ANALYSIS ON THE DUPLICATE FAKE ACCOUNT ON TWITTER USING INTELLIGENT MECHANISM

2021 ◽  
Vol 9 (1) ◽  
pp. 1315-1320
Author(s):  
Dr. Mohammed Ali Alhariri
Keyword(s):  
Social Media ◽  
Random Forest ◽  
Decision Tree ◽  
Machine Learning Techniques ◽  
Social Media Platform ◽  
Twitter Data ◽  
Learning Techniques ◽  
The Social ◽  
Feature Based ◽  
Media Platform

The duplicate fake accounts are detected in this work the data from the social media platform is accessed. The platform choose to use the analysis on social media platform is selected as twitter. The twitter data is accessed using Twitter API, with using some selected features that remain the most appropriate regarding the reason of duplicate fake account. The feature based analysis is compared using machine learning techniques, Random Forest, Decision Tree, and SVM. The performance is further analyzed based on accuracy SVM performed 93.3% accuracy, where decision tree performed as 89.0% and random forest performed as 85.5%. The better performance observed using feature-based analysis is of SVM.  

scholarly journals Biased Random Walk based Social Regularization for Word Embeddings

2018 ◽  
Author(s):  
Ziqian Zeng ◽  
Xin Liu ◽  
Yangqiu Song
Keyword(s):  
Social Media ◽  
Random Walk ◽  
Natural Language ◽  
Language Use ◽  
Personal Characteristics ◽  
Sentiment Classification ◽  
Word Embeddings ◽  
Biased Random Walk ◽  
The Social ◽  
The One

Nowadays, people publish a lot of natural language texts on social media. Socialized word embeddings (SWE) has been proposed to deal with two phenomena of language use: everyone has his/her own personal characteristics of language use and socially connected users are likely to use language in similar ways. We observe that the spread of language use is transitive. Namely, one user can affect his/her friends and the friends can also affect their friends. However, SWE modeled the transitivity implicitly. The social regularization in SWE only applies to one-hop neighbors and thus users outside the one-hop social circle will not be affected directly. In this work, we adopt random walk methods to generate paths on the social graph to model the transitivity explicitly. Each user on a path will be affected by his/her adjacent user(s) on the path. Moreover, according to the update mechanism of SWE, fewer friends a user has, fewer update opportunities he/she can get. Hence, we propose a biased random walk method to provide these users with more update opportunities. Experiments show that our random walk based social regularizations perform better on sentiment classification.

scholarly journals Detection of Sentiment Analysis in Social Media using Deep Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 636-643
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Activation Function ◽  
Sentiment Classification ◽  
Network Layers ◽  
Sentiment Dictionary ◽  
Lstm Network ◽  
The Military

With the rapid climb of web page from social media, such studies as online opinion mining or sentiment analysis of text have started receiving attention from government, industry, and academic sectors. In recent years, sentiment analysis has not only emerged under knowledge fusion within the big data era, but has also become a well-liked research topic within the area of AI and machine learning. This study used the Military life PTT board of Taiwan’s largest online forum because the source of its experimental data. the aim of this study was to construct a sentiment analysis framework and processes for social media so as to propose a self-developed military sentiment dictionary for improving sentiment classification and analyze the performance of various deep learning models with various parameter calibration combinations. The experimental results show that the accuracy and F1-measure of the model that mixes existing sentiment dictionaries and therefore the self-developed military sentiment dictionary are better than the results from using existing sentiment dictionaries only. Furthermore, the prediction model trained using the activation function, Tanh, and when the amount of Bi-LSTM network layers is 2, the accuracy and F1-measure have a good better performance for sentiment classification.

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