scholarly journals A Comparative Analysis of Emotion and Sentiment Analysis Method from Twitter Text

2021 ◽  
Vol 12 (2) ◽  
pp. 1521-1531
Author(s):  
Dr. S. Lavanya, Et. al.
Keyword(s):  
Social Media ◽  
Comparative Analysis ◽  
Sentiment Analysis ◽  
Social Research ◽  
Analysis Method ◽  
Visual Material ◽  
Textual Material ◽  
Important Significance

The Study of Sentiment is an area of science that specializes in the analysis of strong emotions expressed in texts. An opinion is a complete perception of a commodity, service, association, individual or some other form of entity about which a given text is conveyed. This work provides valuable knowledge of the roots of sentiment analysis and how sentiment evaluators can be configured. We demonstrated how to construct a basic classifier and use it as an example. These approaches will eventually change and there will still be the need for a more extensive assessment of emotions. Non-textual material has an important significance in analyses. Photos, photographs, animations and other visual material are also useful in performing social research. Of course, I can see that all these hyperlinks provide essential material. Some other ways of using social media are likes, retweets, reviews on posts and much more! It is hoped that common issues such as avoiding irony and sarcasm would be made less ambiguous. However, there will emerge other issues that will have to be tackled.

scholarly journals Copyright issue on music back sound usage by Indonesian YouTuber

2020 ◽  
Vol 16 (2) ◽  
pp. 137-156
Author(s):  
Xenia Angelica Wijayanto ◽  
Lestari Nurhajati
Keyword(s):  
Social Media ◽  
Young People ◽  
Social Research ◽  
Background Music ◽  
Copyright Issue ◽  
Analysis Method ◽  
Ordinary People ◽  
Unit Of Analysis ◽  
Public Figures ◽  
Copyright Violation

According to We Are Social research in January 2018, Indonesia's population now is 265,4 million, while its social media active users reached 130 million. 43% of people are using Youtube as their primary social media. This situation shows us that Youtube is still the most used and liked social media channel, followed closely by Facebook and Whatsapp. The vlogger phenomenon is also getting stronger among Indonesian young people. The increasing number of Youtube content production ranged from artists, public figures, and ordinary people, also known as Youtuber. One of the famous Youtuber is Raditya Dika, whose subscriber reached more than 3.3 million, and estimated income per year around USD 46 thousand to USD 739 thousand. The problem that arises is about the copyright violation in background music used by Indonesian Youtuber. This research tries to dig further data about the youtube policy in protecting the copyright issue in that area. This research uses a discourse analysis method on 15 videos from the top 5 Youtuber in Indonesia as the unit of analysis. The result shows that some Youtuber still violate the copyright issue while using background music on their Youtube materials production.

Review of Sentiment Detection

2019 ◽  
Vol 11 (1) ◽  
pp. 44-53
Author(s):  
Smiley Gupta ◽  
Jagtar Singh
Keyword(s):  
Social Media ◽  
Comparative Analysis ◽  
Sentiment Analysis ◽  
Large Volume ◽  
User Generated Content ◽  
Automated System ◽  
Social Media Platforms ◽  
Sentiment Detection

A large volume of user-generated data is evolving on a day-to-day basis, especially on social media platforms like Twitter, where people express their opinions and emotions regarding certain individuals or entities. This user-generated content becomes very difficult to analyze manually and therefore requires a need for an intelligent automated system which mines the opinions and organizes them using polarity metrics, representing the process of sentiment analysis. The motive of this review is to study the concept of sentiment analysis and discuss the comparative analysis of its techniques along with the challenges in this field to be considered for future enhancement.

scholarly journals KEBUNTUAN PEMAHAMAN TERHADAP POLEMIK WACANA ISLAM NUSANTARA: KAJIAN DARI PERSPEKTIF MAKNA PRAGMATIK

2020 ◽  
Vol 3 (2) ◽  
pp. 143-154
Author(s):  
Taufik Nurhadi
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Comparative Analysis ◽  
Analysis Method ◽  
The Core ◽  
Pros And Cons ◽  
Worship Practices ◽  
Symbolic Identity ◽  
Constant Comparative Analysis ◽  
Core Problem

This study is aimed at describing of the impasse understanding of the polemic of the Nusantara Islamic discourse. The data were in the form of Indonesian language used in the polemic of the pros and cons of the Nusantara Islam issue, which is published on social media, Youtube. Data collection uses the method of listening with tapping, SBLC, download, and note techniques. The data analysis method used is Constant Comparative Analysis. The results of the analysis showed that there was a deadlock in understanding the issues of the Nusantara Islamic discourse regarding 4 things, namely symbolic identity, rejection in terms of terms, classic rivalry between tribes, and culture as the core problem. The deadlock of understanding was triggered by long competition between the two circles of Muhammadiyah and Nahdatul Ulama in viewing worship practices from different perspectives.

Sentiment analysis: a review and comparative analysis over social media

2018 ◽  
Vol 11 (1) ◽  
pp. 97-117 ◽  
Author(s):  
Nikhil Kumar Singh ◽  
Deepak Singh Tomar ◽  
Arun Kumar Sangaiah
Keyword(s):  
Social Media ◽  
Comparative Analysis ◽  
Sentiment Analysis

scholarly journals Lexicon based Chinese language sentiment analysis method

2019 ◽  
Vol 16 (2) ◽  
pp. 639-655
Author(s):  
Jinyan Chen ◽  
Susanne Becken ◽  
Bela Stantic
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Chinese Language ◽  
Big Data Analytics ◽  
Limited Attention ◽  
Analysis Method ◽  
Media Platform ◽  
Chinese Social Media

The growing number of social media users and vast volume of posts could provide valuable information about the sentiment toward different locations, services as well as people. Recent advances in Big Data analytics and natural language processing often means to automatically calculate sentiment in these posts. Sentiment analysis is challenging and computationally demanding task due to the volume of data, misspelling, emoticons as well as abbreviations. While significant work was directed toward the sentiment analysis of English text there is limited attention in literature toward the sentiment analytic of Chinese language. In this work we propose method to identify the sentiment in Chinese social media posts and to test our method we rely on posts sent by visitors of Great Barrier Reef by users of most popular Chinese social media platform Sina Weibo. We elaborate process of capturing of weibo posts, describe a creation of lexicon as well as develop and explain algorithm for sentiment calculation. In case study, related to sentiment toward the different GBR destinations, we demonstrate that the proposed method is effective in obtaining the information and is suitable to monitor visitors? opinion.

scholarly journals Toward multi-label sentiment analysis: a transfer learning based approach

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Jie Tao ◽  
Xing Fang
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Language Processing ◽  
State Of The Art ◽  
Business Owners ◽  
Analysis Method ◽  
Learning Models ◽  
Analytical Problem ◽  
Model Training

AbstractSentiment analysis is recognized as one of the most important sub-areas in Natural Language Processing (NLP) research, where understanding implicit or explicit sentiments expressed in social media contents is valuable to customers, business owners, and other stakeholders. Researchers have recognized that the generic sentiments extracted from the textual contents are inadequate, thus, Aspect Based Sentiment Analysis (ABSA) was coined to capture aspect sentiments expressed toward specific review aspects. Existing ABSA methods not only treat the analytical problem as single-label classification that requires a fairly large amount of labelled data for model training purposes, but also underestimate the entity aspects that are independent of certain sentiments. In this study, we propose a transfer learning based approach tackling the aforementioned shortcomings of existing ABSA methods. Firstly, the proposed approach extends the ABSA methods with multi-label classification capabilities. Secondly, we propose an advanced sentiment analysis method, namely Aspect Enhanced Sentiment Analysis (AESA) to classify text into sentiment classes with consideration of the entity aspects. Thirdly, we extend two state-of-the-art transfer learning models as the analytical vehicles of multi-label ABSA and AESA tasks. We design an experiment that includes data from different domains to extensively evaluate the proposed approach. The empirical results undoubtedly exhibit that the proposed approach outperform all the baseline approaches.

scholarly journals A Novel Integrated Framework for Sarcasm Detection In Social Platform

2020 ◽  
Vol 9 (4) ◽  
pp. 943-950
Keyword(s):  
Social Media ◽  
Comparative Analysis ◽  
Sentiment Analysis ◽  
Performance Measure ◽  
Online Forum ◽  
Integrated Framework ◽  
Analysis Process

Sarcasm is a form of speech which transforms the verbatim meaning of a sentence into its antonym. Sarcasm identification in social media is a crucial facet of the sentiment analysis process, since it deals with texts whose polarity is completely opposite from its utterance. Our paper provides an exhaustive review of the existing methodologies dedicated to the task of detecting sarcasm in texts posted on an online forum. A comparative analysis of the existing techniques, mentioning the datasets and the performance measure, is also provided. This paper also introduces a novel integrated framework for identifying sarcastic clues in tweets, and recognizing sarcastic users.

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