SentiCR: A customized sentiment analysis tool for code review interactions

2017 ◽  
Author(s):  
Toufique Ahmed ◽  
Amiangshu Bosu ◽  
Anindya Iqbal ◽  
Shahram Rahimi
Keyword(s):  
Sentiment Analysis ◽  
Analysis Tool ◽  
Code Review

A sentiment analysis system for social media using machine learning techniques: Social enablement

2018 ◽  
Vol 34 (3) ◽  
pp. 569-581 ◽  
Author(s):  
Sujata Rani ◽  
Parteek Kumar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Tool ◽  
Data Set ◽  
Learning Techniques

Abstract In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots.

Employing the Sentiment Analysis Tool in NVivo 11 Plus on Social Media Data

2017 ◽  
pp. 175-244
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Analysis Tool ◽  
Attitudes And Beliefs ◽  
Social Media Data ◽  
Public Issues ◽  
Public Events ◽  
Brand Reputation ◽  
Text Corpora ◽  
Media Data

Sentiment analysis has been used to assess people's feelings, attitudes, and beliefs, ranging from positive to negative, on a variety of phenomena. Several new autocoding features in NVivo 11 Plus enable the capturing of sentiment analysis and extraction of themes from text datasets. This chapter describes eight scenarios in which these tools may be applied to social media data, to (1) profile egos and entities, (2) analyze groups, (3) explore metadata for latent public conceptualizations, (4) examine trending public issues, (5) delve into public concepts, (6) observe public events, (7) analyze brand reputation, and (8) inspect text corpora for emergent insights.

Security Smells in Ansible and Chef Scripts

2021 ◽  
Vol 30 (1) ◽  
pp. 1-31
Author(s):  
Akond Rahman ◽  
Md Rayhanur Rahman ◽  
Chris Parnin ◽  
Laurie Williams
Keyword(s):  
Empirical Study ◽  
Qualitative Analysis ◽  
Static Analysis ◽  
Open Source Software ◽  
Analysis Tool ◽  
Prior Work ◽  
Code Review ◽  
Bug Reports ◽  
Case Statement ◽  
Static Analysis Tool

Context: Security smells are recurring coding patterns that are indicative of security weakness and require further inspection. As infrastructure as code (IaC) scripts, such as Ansible and Chef scripts, are used to provision cloud-based servers and systems at scale, security smells in IaC scripts could be used to enable malicious users to exploit vulnerabilities in the provisioned systems. Goal: The goal of this article is to help practitioners avoid insecure coding practices while developing infrastructure as code scripts through an empirical study of security smells in Ansible and Chef scripts. Methodology: We conduct a replication study where we apply qualitative analysis with 1,956 IaC scripts to identify security smells for IaC scripts written in two languages: Ansible and Chef. We construct a static analysis tool called Security Linter for Ansible and Chef scripts (SLAC) to automatically identify security smells in 50,323 scripts collected from 813 open source software repositories. We also submit bug reports for 1,000 randomly selected smell occurrences. Results: We identify two security smells not reported in prior work: missing default in case statement and no integrity check. By applying SLAC we identify 46,600 occurrences of security smells that include 7,849 hard-coded passwords. We observe agreement for 65 of the responded 94 bug reports, which suggests the relevance of security smells for Ansible and Chef scripts amongst practitioners. Conclusion: We observe security smells to be prevalent in Ansible and Chef scripts, similarly to that of the Puppet scripts. We recommend practitioners to rigorously inspect the presence of the identified security smells in Ansible and Chef scripts using (i) code review, and (ii) static analysis tools.

Forecasting Oil Price Trends with Sentiment of Online News Articles

2017 ◽  
Vol 34 (02) ◽  
pp. 1740019 ◽  
Author(s):  
Jian Li ◽  
Zhenjing Xu ◽  
Huijuan Xu ◽  
Ling Tang ◽  
Lean Yu
Keyword(s):  
Big Data ◽  
Sentiment Analysis ◽  
Rapid Development ◽  
Online News ◽  
Oil Price ◽  
Support Vector ◽  
Analysis Tool ◽  
Trend Prediction ◽  
Oil Markets ◽  
Price Trends

With the rapid development of the Internet and big data technologies, a rich of online data (including news releases) can helpfully facilitate forecasting oil price trends. Accordingly, this study introduces sentiment analysis, a useful big data analysis tool, to understand the relevant information of online news articles and formulate an oil price trend prediction method with sentiment. Three main steps are included in the proposed method, i.e., sentiment analysis, relationship investigation and trend prediction. In sentiment analysis, the sentiment (or tone) is extracted based on a dictionary-based approach to capture the relevant online information concerning oil markets and the driving factors. In relationship investigation, the Granger causality analysis is conducted to explore whether and how the sentiment impacts oil price. In trend prediction, the sentiment is used as an important independent variable, and some popular forecasting models, e.g., logistic regression, support vector machine, decision tree and back propagation neural network, are performed. With crude oil futures prices of the West Texas Intermediate (WTI) and news articles of the Thomson Reuters as studying samples, the empirical results statistically support the powerful predictive power of sentiment for oil price trends and hence the effectiveness of the proposed method.

scholarly journals An Overview of Tools and Technologies Used for Opinion Mining and Sentiment Analysis

2021 ◽  
pp. 38-41
Author(s):  
Dr. A. Komathi ◽  
P. Nithya
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Vital Role ◽  
Unstructured Data ◽  
Analysis Tool ◽  
Challenging Problem ◽  
Huge Amount ◽  
Product Sale ◽  
Product Service ◽  
Consumer's Perception

The endeavor of social media has formed many chances for people to publicly voice their beliefs, simply when they are employed to deliver an opinion hit a vital problem. Sentiment analysis is the process to finding the satisfaction information of a consumer’s perception about product, service or brand. Sentiment analysis is also called as opinion mining because it dealt with the huge amount of customer opinion. The analyzing process of customer opinion is playing a vital role in product sale. Sentiment analysis is to extract the features by the notions from others perception about particular product and buying experience. The Sentiment Analysis tool is to function on a series of expressions for a given item based on the quality and features.. To find the opinion rate in the form of unstructured data is been a challenging problem today. Thus, this paper discusses about Sentiment analysis methods and tools which are used to make clear opinion mining.

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