Learning sentiment from students’ feedback for real-time interventions in classrooms

Knowledge about users sentiments can be used for a variety of adaptation purposes. In the case of teaching, knowledge about students sentiments can be used to address problems like confusion and boredom which affect students engagement. For this purpose, we looked at several methods that could be used for learning sentiment from students feedback. Thus, Naive Bayes, Complement Naive Bayes (CNB), Maximum Entropy and Support Vector Machine (SVM) were trained using real students' feedback. Two classifiers stand out as better at learning sentiment, with SVM resulting in the highest accuracy at 94%, followed by CNB at 84%. We also experimented with the use of the neutral class and the results indicated that, generally, classifiers perform better when the neutral class is excluded

Sentiment analysis:towards a tool for analysing real-time students feedback

Students' real-time feedback has numerous advantages in education, however, analysing feedback while teaching is both stressful and time consuming. To address this problem, we propose to analyse feedback automatically using sentiment analysis. Sentiment analysis is domain dependent and although it has been applied to the educational domain before, it has not been previously used for real-time feedback. To find the best model for automatic analysis we look at four aspects: preprocessing, features, machine learning techniques and the use of the neutral class. We found that the highest result for the four aspects is Support Vector Machines (SVM) with the highest level of preprocessing, unigrams and no neutral class, which gave a 95 percent accuracy

Tourism Companies Assessment via Social Media Using Sentiment Analysis

ازدادت وسائل التواصل الاجتماعي بشكل كبير وواضح لانها وسيلة إعلام للمستخدمين للتعبير عن مشاعرهم من خلال آلاف المنشورات والتعليقات حول شركات السياحة. وبالتالي ، يصعب على السائح قراءة جميع التعليقات لتحديد ما إذا كانت تلك الآراء إيجابية أم سلبية لتقييم نجاح الشركة. في هذه البحث,تم استخدام التنقيب عن النص لتصنيف المشاعر من خلال جمع مراجعات اللهجة العراقية حول شركات السياحة من الفيس بوك لتحليلها باستخدام تحليل المشاعر لتتبع المشاعر الموجوده في المنشورات والتعليقات. ثم تم تصنيفها إلى تعليق إيجابي أو سلبي أو محايد باستخدام Naïve Bayes, Rough Set Theory , K-Nearest Neighbor. من بين 71 شركة سياحة عراقية وجدت أن 28٪ من هذه الشركات لديها تقييم جيد جدا ، و 26٪ من هذه الشركات لديها تقييم جيد ، و 31٪ من هذه الشركات لديها تقييم متوسط ​​، و 4٪ من هذه الشركات لديها تقييم مقبول و 11٪ من هذه الشركات لديها تقييم سيء. ساعدت النتائج التجريبية الشركات على تحسين عملها وبرامجها واستجابة كافية وسريعة لمتطلبات العملاءIn recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor methods. After experimental results, it was determined that out of 71 tested Iraqi tourism companies, 28% from these companies have very good assessment, 26% from these companies have good assessment, 31% from these companies have medium assessment, 4% from these companies have acceptance assessment and 11% from these companies have bad assessment. These results helped the companies to improve their work and programs responding sufficiently and quickly to customer demands

Analisa Pengaruh Iklan tanpa Label Harga pada media sosial Menggunakan Algoritma Naive Bayes

Media sosial seperti  Facebook, Instagram, Whatsapps sangat digemari oleh para pelaku bisnis untuk memasarkan produknya secara online. Berbagai macam metode dalam beriklan dilakukan oleh para pebisnis online, salah satunya adalah iklan tanpa label harga. Akan tetapi, iklan tanpa label harga ini sempat menuai pro dan kontra diantara calon pembeli karena pada sebagian calon pembeli merasa tidak nyaman. Sedangkan dari sisi penjual, hal ini merupakan trik pemasaran untuk meningkatkan penjualan produknya. Penelitian ini bertujuan menganalisa keputusan calon konsumen terhadap iklan tanpa label harga. Metode penelitian yang digunakan pada penelitian ini adalah algoritma klasifikasi data mining Algoritma Naive Bayes. Sedangkan, untuk metode penerapan data mining menggunakan metodologi Knowledge Discovery of Database (KDD). Adapun, sumber data dalam penelitian ini diperoleh dari hasil sebaran quisioner terhadap 203 responden pengguna media sosil aktif dengan atribut penelitian berdasarkan gender, attention, interest , search dan Action. Setelah melakukan tahapan proses KDD pada data mining menggunakan algoritma Naive Bayes diperoleh akurasi sebesar 97,6%. Berdasarkan hasil penelitian tersebut, dapat disimpulkan bahwa iklan tanpa label harga pada media sosial dapat mempengaruhi keputusan pembelian. .   &nbsp

Customer Behavior Analysis for Social Media

It is essential for a business organization to get the customer feedback in order to grow as a company. Business organizations are collecting customer feedback using various methods. But the question is ‘are they efficient and effective?' In the current context, there is more of a customer oriented market and all the business organizations are competing to achieve customer delight through their products and services. Social Media plays a huge role in one's life. Customers tend to reveal their true opinion about certain brands on social media rather than giving routine feedback to the producers or sellers. Because of this reason, it is identified that social media can be used as a tool to analyze customer behavior. If relevant data can be gathered from the customers' social media feeds and if these data are analyzed properly, a clear idea to the companies what customers really think about their brand can be provided

Supervised Learning for Multi-Domain Text Classification

Digital information available on the Internet is increasing day by day. As a result of this, the demand for tools that help people in finding and analyzing all these resources are also growing in number. Text Classification, in particular, has been very useful in managing the information. Text Classification is the process of assigning natural language text to one or more categories based on the content. It has many important applications in the real world. For example, finding the sentiment of the reviews, posted by people on restaurants, movies and other such things are all applications of Text classification. In this project, focus has been laid on Sentiment Analysis, which identifies the opinions expressed in a piece of text. It involves categorizing opinions in text into categories like \u27positive\u27 or \u27negative\u27. Existing works in Sentiment Analysis focused on determining the polarity (Positive or negative) of a sentence. This comes under binary classification, which means classifying the given set of elements into two groups. The purpose of this research is to address a different approach for Sentiment Analysis called Multi Class Sentiment Classification. In this approach the sentences are classified under multiple sentiment classes like positive, negative, neutral and so on. Classifiers are built on the Predictive Model, that consists of multiple phases. Analysis of different sets of features on the data set, like stemmers, n-grams, tf-idf and so on, will be considered for classification of the data. Different classification models like Bayesian Classifier, Random Forest and SGD classifier are taken into consideration for classifying the data and their results are compared. Frameworks like Weka, Apache Mahout and Scikit are used for building the classifiers

Identifikasi Kebutuhan Masyarakat Nusa Tenggara Barat pada Pandemi Covid-19 di Media Sosial dengan Metode Crawling

People can write to the government about what they want through social media in the freedom of information era. Especially in the Covid-19 pandemic, the public's desire for government attention is getting higher. The purpose of this activity is to collect community needs at the beginning of the pandemic. The period of information retrieval is from March 30 to April 5, 2020. The activity uses the crawling method, which is gathering information with browser tools. The implementation stage starts from taking information with selenium, identifying posts and comments with Html tags, calculating the emergence of issues with N-gram NLP, and analyzing sentiment with Naive Bayes and Support-Vector Machine. The results showed that the community did not have panic to need foods with a total reaction of 172. The community was more worried about the spread of outbreaks from outside NTB with a reaction of 1,421

Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

The big five: Discovering linguistic characteristics that typify distinct personality traits across Yahoo! answers members

Indexación: Scopus.This work was partially supported by the project FONDECYT “Bridging the Gap between Askers and Answers in Community Question Answering Services” (11130094) funded by the Chilean Government.In psychology, it is widely believed that there are five big factors that determine the different personality traits: Extraversion, Agreeableness, Conscientiousness and Neuroticism as well as Openness. In the last years, researchers have started to examine how these factors are manifested across several social networks like Facebook and Twitter. However, to the best of our knowledge, other kinds of social networks such as social/informational question-answering communities (e.g., Yahoo! Answers) have been left unexplored. Therefore, this work explores several predictive models to automatically recognize these factors across Yahoo! Answers members. As a means of devising powerful generalizations, these models were combined with assorted linguistic features. Since we do not have access to ask community members to volunteer for taking the personality test, we built a study corpus by conducting a discourse analysis based on deconstructing the test into 112 adjectives. Our results reveal that it is plausible to lessen the dependency upon answered tests and that effective models across distinct factors are sharply different. Also, sentiment analysis and dependency parsing proven to be fundamental to deal with extraversion, agreeableness and conscientiousness. Furthermore, medium and low levels of neuroticism were found to be related to initial stages of depression and anxiety disorders. © 2018 Lithuanian Institute of Philosophy and Sociology. All rights reserved.https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/275