scholarly journals Analys is and Creation of Free Sentiment Analysis Programs

2019 ◽  
Vol 25 (1) ◽  
pp. 83-105
Author(s):  
Josip Mihaljević
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks ◽  
Web Search ◽  
The Internet ◽  
Pilot Program ◽  
Online Programs ◽  
Input Text ◽  
Second Category ◽  
Pie Chart

This paper analyzes free online programs for sentiment analysis which can, on the bases of their algorithm, give a positive, negative or neutral opinion of a text. At the beginning of the paper sentiment analysis programs and techniques they use such as Naive Bayes and Recurrent Neural Networks are presented. The programs are divided into two categories for analysis. The fi rst category consists of sentiment analysis programs which analyze texts written or copied inside the user interface. The second category consists of programs for analyzing opinions posted on social networks, blogs, and other media sites. Programs from both categories were chosen for this research on the bases of positive reviews on computer science portals and their popularity on web search engin es such as Google and Bing. The accuracy of the programs from the fi rst category was checked by inserting the same sentence from movie reviews and comparing the results. Their additional options have also been analyzed. For the second category of programs, it was determined which social networks, blogs, and other social media they cover on the internet. The purpose of this analysis was to check the overall quality and options that free sentiment analysis programs provide. An example of how to create one’s own custom sentiment analyzer by using the available Python code and libraries found online is also given. Two simple programs were created using Python. The fi rst program belongs to the fi rst category of programs for analyzing an input text. This program serves as a pilot program for Croatian which gives only the basic analysis of sentences. The second program collects recent tweets from Twitter containing certain words and creates a pie chart based on the analysis of the results.

Fake News Polarization for Sentiment Analysis

2022 ◽  
pp. 255-263
Author(s):  
Chirag Visani ◽  
Vishal Sorathiya ◽  
Sunil Lavadiya
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Opinion Mining ◽  
Easy Access ◽  
The Internet ◽  
Fake News ◽  
Social Media Networks ◽  
Sentiment Mining

The popularity of the internet has increased the use of e-commerce websites and news channels. Fake news has been around for many years, and with the arrival of social media and modern-day news at its peak, easy access to e-platform and exponential growth of the knowledge available on social media networks has made it intricate to differentiate between right and wrong information, which has caused large effects on the offline society already. A crucial goal in improving the trustworthiness of data in online social networks is to spot fake news so the detection of spam news becomes important. For sentiment mining, the authors specialise in leveraging Facebook, Twitter, and Whatsapp, the most prominent microblogging platforms. They illustrate how to assemble a corpus automatically for sentiment analysis and opinion mining. They create a sentiment classifier using the corpus that can classify between fake, real, and neutral opinions in a document.

scholarly journals PREPROCESSING ARABIC DIALECT FOR SENTIMENT MINING: STATE OF ART

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 323-330
Author(s):  
Z. Nassr ◽  
N. Sael ◽  
F. Benabbou
Keyword(s):  
Social Networks ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
The Internet ◽  
Sentiment Mining ◽  
Arabic Dialects ◽  
Limited List ◽  
Processing Solution

Abstract. Sentiment Analysis concerns the analysis of ideas, emotions, evaluations, values, attitudes and feelings about products, services, companies, individuals, tasks, events, titles and their characteristics. With the increase in applications on the Internet and social networks, Sentiment Analysis has become more crucial in the field of text mining research and has since been used to explore users’ opinions on various products or topics discussed on the Internet. Developments in the fields of Natural Language Processing and Computational Linguistics have contributed positively to Sentiment Analysis studies, especially for sentiments written in non-structured or semi-structured languages. In this paper, we present a literature review on the pre-processing task on the field of sentiment analysis and an analytical and comparative study of different researches conducted in Arabic social networks. This study allowed as concluding that several works have dealt with the generation of stop words dictionary. In this context, two approaches are adopted: first, the manual one, which gives rise to a limited list, and second, the automatic, where the list of stop words is extracted from social networks based on defined rules. For stemming two, algorithms have been proposed to isolate prefixes and suffixes from words in dialects. However, few works have been interested in dialects directly without translation. The Moroccan dialect in particular is considered as the 5th dialect studied among Arabic dialects after Jordanian, Egyptian, Tunisian and Algerian dialects. Despite the significant lack in studies carried out on Arabic dialects, we were able to extract several conclusions about the difficulties and challenges encountered through this comparative study, as well as the possible ways and tracks to study in any dialects sentiment analysis pre-processing solution.

scholarly journals Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms

2018 ◽  
Vol 6 ◽  
pp. 687-702 ◽  
Author(s):  
Wenpeng Yin ◽  
Hinrich Schütze
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks ◽  
Representation Learning ◽  
Local Context ◽  
Fixed Size ◽  
Convolution Operation ◽  
Input Text ◽  
Multiple Context ◽  
Textual Entailment

In NLP, convolutional neural networks (CNNs) have benefited less than recurrent neural networks (RNNs) from attention mechanisms. We hypothesize that this is because the attention in CNNs has been mainly implemented as attentive pooling (i.e., it is applied to pooling) rather than as attentive convolution (i.e., it is integrated into convolution). Convolution is the differentiator of CNNs in that it can powerfully model the higher-level representation of a word by taking into account its local fixed-size context in the input text t x. In this work, we propose an attentive convolution network, ATTCONV. It extends the context scope of the convolution operation, deriving higher-level features for a word not only from local context, but also from information extracted from nonlocal context by the attention mechanism commonly used in RNNs. This nonlocal context can come (i) from parts of the input text t x that are distant or (ii) from extra (i.e., external) contexts t y. Experiments on sentence modeling with zero-context (sentiment analysis), single-context (textual entailment) and multiple-context (claim verification) demonstrate the effectiveness of ATTCONV in sentence representation learning with the incorporation of context. In particular, attentive convolution outperforms attentive pooling and is a strong competitor to popular attentive RNNs. 1

Aplikasi Pemantauan Media Sosial untuk Analisa Merek

2021 ◽  
Vol 13 (1) ◽  
pp. 1-6
Author(s):  
Rikip Ginanjar ◽  
Rosalina Rosalina ◽  
Aldo Wijaya
Keyword(s):  
Sentiment Analysis ◽  
The Internet ◽  
Android Application ◽  
Monthly Report ◽  
Index Terms ◽  
Conventional Methods ◽  
Pie Chart

Abstract— In recent years, micro-blogs on the Internet have become a popular way of expressing feelings, thoughts, and even communicating opinions about products and services that are common among its users. Collecting user opinions can be an expensive and time-consuming task using conventional methods such as surveys. The sentiment analysis of the customer opinions makes it easier for businesses to understand their competitive value in a changing market and to understand their customer views about their products and services. In this research, Lexicon-Based approach especially AFINN lexicon is implemented to classify user twitter sentiment, throughout which, twitter Micro-blogs data has been collected, pre-processed analyzed, and classified. The results of this research is an android application that could classify users' perspective via tweets into positive and negative, which is represented in a pie chart for Monthly report. Index Terms— Sentiment Analysis, Brand Analysis, Twitter, Android Application

Approaches and Applications for Sentiment Analysis

2022 ◽  
pp. 1-23
Author(s):  
M. Govindarajan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Supervised Machine Learning ◽  
The Internet ◽  
Digital Form ◽  
Clear Cut ◽  
Consumer Review ◽  
Networks Analysis

With the increasing penetration of the internet, an ever-growing number of people are voicing their opinions in the numerous blogs, tweets, forums, social networking, and consumer review websites. Each such opinion has a sentiment (positive, negative, or neutral) associated with it. But the problem is that the amount of data is simply overwhelming. Methods like supervised machine learning and lexical-based approaches are available for measuring sentiments that have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services. This chapter presents sentiment analysis applications and challenges with their approaches and tools. The techniques and applications discussed in this chapter will provide a clear-cut idea to the sentiment analysis researchers to carry out their work in this field.

Update of the last 2 years on the impact of the internet and social networks on the management of type 2 diabetes mellitus: simple review. (Preprint)

2020 ◽  
Author(s):  
Mayli Lañas-Navarro ◽  
Jose Ipanaque-Calderon Sr ◽  
Fiorela E Solano
Keyword(s):  
Social Networks ◽  
Mobile Applications ◽  
Health Personnel ◽  
Online Courses ◽  
The Internet ◽  
Diabetic Patients ◽  
Bibliographic Search ◽  
Use Of The Internet ◽  
And Control ◽  
The Impact

BACKGROUND Research on the use of the Internet in the medical field is experiencing many advances, including mobile applications, social networks, telemedicine. Its implementation in medical care and comprehensive patient management is a much discussed topic at present. OBJECTIVE This narrative review aims to understand the impact of the internet and social networks on the management of diabetes, both for patients and medical staff. METHODS The bibliographic search was carried out in the databases Pubmed, Virtual Health Library (VHL) and Lilacs between 2018 to 2020. RESULTS Multiple mobile applications have been created for the help and control of diabetic patients, as well as the implementation of online courses, improving the knowledge of health personnel applying them in the field of telemedicine. CONCLUSIONS The use of the Internet and social networks brings many benefits for both the diabetic patient and the health personnel, offering advantages for both.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

Social Exclusion and Transport in the UK: A Role for Virtual Accessibility in the Alleviation of Mobility-Related Social Exclusion?

2003 ◽  
Vol 32 (3) ◽  
pp. 317-338 ◽  
Author(s):  
SUSAN KENYON ◽  
JACKIE RAFFERTY ◽  
GLENN LYONS
Keyword(s):  
Social Networks ◽  
Social Policy ◽  
Social Exclusion ◽  
Causal Link ◽  
Public Consultation ◽  
The Internet ◽  
Physical Mobility ◽  
Viable Solution ◽  
Virtual Mobility ◽  
The Uk

This paper reports findings from research into the possibility that mobility-related social exclusion could be affected by an increase in access to virtual mobility – access to opportunities, services and social networks, via the Internet – amongst populations that experience exclusion. Transport is starting to be recognised as a key component of social policy, particularly in light of a number of recent studies, which have highlighted the link between transport and social exclusion, suggesting that low access to mobility can reduce the opportunity to participate in society – a finding with which this research concurs. Following the identification of this causal link, the majority of studies suggest that an increase in access to adequate physical mobility can provide a viable solution to mobility-related aspects of social exclusion.This paper questions the likelihood that increased physical mobility can, by itself, provide a fully viable or sustainable solution to mobility-related aspects of social exclusion. Findings from both a desk study and public consultation suggest that virtual mobility is already fulfilling an accessibility role, both substituting for and supplementing physical mobility, working to alleviate some aspects of mobility-related social exclusion in some sectors of society. The paper incorporates an analysis of the barriers to and problems with an increase in virtual mobility in society, and concludes that virtual mobility could be a valuable tool in both social and transport policy.

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