scholarly journals Stage Performance and Musical Analysis of Haacaaluu Hundeessaa Works by using Sentiment Analysis Techniques

2021 ◽  
pp. 124-128
Author(s):  
Gemechu Boche Beshana
Keyword(s):  
Social Media ◽  
Civil Rights ◽  
Sentiment Analysis ◽  
Musical Analysis ◽  
Text Data ◽  
Analysis Techniques ◽  
Good Performer ◽  
Musical Works ◽  
Stage Performance ◽  
Tube Channel

This research is initiated to analysis the stage performance and musical analysis of Haacaaluu Hundeessaa works on Afaan oromoo reviews text collected from social Medias by applying the concept of sentiment analysis techniques. The data (reviews) were collected manually from social media on Haacaaluu Hundeessaa musical works. Haacaaluu Hundeessaa is Oromo singer, songwriter, civil rights activist and his being a voice his people. He is the most famous young singer, hero and icon of Oromo musician. From his musical works Sanyii Mootii(Race of the King), Waa'ee Keenyaa(Our Plight), Maalan Jira(What existence is mine), JIRTUU? are the most popular known in Oromo peoples as well as nation and nationality of Ethiopia. From Hacaaluu musical works, the researcher considered reviews (opinions) afaan oromoo text data on his single sang “JIRTUU?” to analysis stage performance of Haacaaluu Hundeessaa’s. The researcher inspires to use this single sang because; reviews (comments) are easier to access, available on you tube channel, extensive in size relatively from other his songs from social media. Additionally this song was broadcast live by Oromia Broadcasting Network and other Medias on different stage. Opinion lexicon approach with contextual valence shifter is used for analyzing opinions (comments) given on stage performance of Haacaaluu Hundeessaa works. Totally 18,687 reviews opinions or sentences were collected for experimentations to analysis stage performance and musical works of Haacaaluu Hundeessaa. Accordingly our result indicates around 99.52 percents peoples likes the stage performance and musical works of Hacaaluu Hundeessaa. From the results researcher observed Haacaaluu Hundeessaa has good performer with sing emotional and physically when he sing on the stages. In future works, further research needs to study others musical works of Hacaaluu Hundeessaa.

A Survey on Sentiment Analysis Techniques for Twitter

2022 ◽  
pp. 57-90
Author(s):  
Surabhi Verma ◽  
Ankit Kumar Jain
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Analysis Techniques ◽  
Goods And Services ◽  
Text Document ◽  
The Subject ◽  
Over Time

People regularly use social media to express their opinions about a wide variety of topics, goods, and services which make it rich in text mining and sentiment analysis. Sentiment analysis is a form of text analysis determining polarity (positive, negative, or neutral) in text, document, paragraph, or clause. This chapter offers an overview of the subject by examining the proposed algorithms for sentiment analysis on Twitter and briefly explaining them. In addition, the authors also address fields related to monitoring sentiments over time, regional view of views, neutral tweet analysis, sarcasm detection, and various other tasks in this area that have drawn the researchers ' attention to this subject nearby. Within this chapter, all the services used are briefly summarized. The key contribution of this survey is the taxonomy based on the methods suggested and the debate on the theme's recent research developments and related fields.

Summarizing Opinions with Sentiment Analysis from Multiple Reviews on Travel Destinations

2018 ◽  
Vol 9 (2) ◽  
pp. 111-120
Author(s):  
Argha Roy ◽  
Shyamali Guria ◽  
Suman Halder ◽  
Sayani Banerjee ◽  
Sourav Mandal
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Efficient Method ◽  
Text Analysis ◽  
Human Life ◽  
Analysis Techniques ◽  
Classification Technique ◽  
Textual Data ◽  
The Web

Recently, the web has been crowded with growing volumes of various texts on every aspect of human life. It is difficult to rapidly access, analyze, and compose important decisions using efficient methods for raw textual data in the form of social media, blogs, feedback, reviews, etc., which receive textual inputs directly. It proposes an efficient method for summarization of various reviews of tourists on a specific tourist spot towards analyzing their sentiments towards the place. A classification technique automatically arranges documents into predefined categories and a summarization algorithm produces the exact condensed input such that output is most significant concepts of source documents. Finally, sentiment analysis is done in summarized opinion using NLP and text analysis techniques to show overall sentiment about the spot. Therefore, interested tourists can plan to visit the place do not go through all the reviews, rather they go through summarized documents with the overall sentiment about target place.

scholarly journals Ternary Classification of Product Based Reviews: Survey, Open Issues and New Approach for Sentiment Analysis

2021 ◽  
Vol 1 (2) ◽  
pp. 1-8
Author(s):  
Sushila Sonare ◽  
Megha Kamble
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Text Data ◽  
New Approach ◽  
Meaningful Information ◽  
Multi Class Classification ◽  
Almost All ◽  
Open Issues

Now-a-days, it is very common that the customers share their thoughts about any product, brand and their experience in social media. The analysts collect these reviews and process it, to extract meaningful information about the product. The beauty of social media is, it’s involved in all the domains. So the analysts got reviews from different social media and platforms for almost all kind of thing. The Sentiment Analysis is applied to predict outcomes for getting useful information, for ex.; like predict the blockbuster for a movie, rating for any new launches and many more. This type of prediction is really helpful for the customer to buy any goods or take any services in this competitive world. This paper is focused on e-commerce website reviews which are normally in text form with some special characters and some symbols (emojis). Each word in this text set got some meaning in terms of context, emotion and prior experience. These characteristics contribute to some of the features of text data for prediction. The objective of this paper is to compile existing research works on text analysis and emotion based analysis. The open issues and challenges of document based sentiment analysis are also discussed. The paper concluded with proposing a new approach of multi class classification. Ternary classification for classes positive, negative and neutral is suggested primarily for product based text and emoji reviews on Twitter social media.

scholarly journals Sentiment Analysis of Social Survey Data for Local City Councils

2022 ◽  
Vol 11 (1) ◽  
pp. 7
Author(s):  
Marianna Lepelaar ◽  
Adam Wahby ◽  
Martha Rossouw ◽  
Linda Nikitin ◽  
Kanewa Tibble ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Survey Data ◽  
Smart Cities ◽  
Social Indicators ◽  
Media Analysis ◽  
Social Surveys ◽  
Analysis Techniques ◽  
The Social ◽  
The City

Big data analytics can be used by smart cities to improve their citizens’ liveability, health, and wellbeing. Social surveys and also social media can be employed to engage with their communities, and these can require sophisticated analysis techniques. This research was focused on carrying out a sentiment analysis from social surveys. Data analysis techniques using RStudio and Python were applied to several open-source datasets, which included the 2018 Social Indicators Survey dataset published by the City of Melbourne (CoM) and the Casey Next short survey 2016 dataset published by the City of Casey (CoC). The qualitative nature of the CoC dataset responses could produce rich insights using sentiment analysis, unlike the quantitative CoM dataset. RStudio analysis created word cloud visualizations and bar charts for sentiment values. These were then used to inform social media analysis via the Twitter application programming interface. The R codes were all integrated within a Shiny application to create a set of user-friendly interactive web apps that generate sentiment analysis both from the historic survey data and more immediately from the Twitter feeds. The web apps were embedded within a website that provides a customisable solution to estimate sentiment for key issues. Global sentiment was also compared between the social media approach and the 2016 survey dataset analysis and showed some correlation, although there are caveats on the use of social media for sentiment analysis. Further refinement of the methodology is required to improve the social media app and to calibrate it against analysis of recent survey data.

scholarly journals Diabetes on Twitter: A Sentiment Analysis

2018 ◽  
Vol 13 (3) ◽  
pp. 439-444 ◽  
Author(s):  
Elia Gabarron ◽  
Enrique Dorronzoro ◽  
Octavio Rivera-Romero ◽  
Rolf Wynn
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Text Message ◽  
Positive Association ◽  
Health Condition ◽  
Media Impact ◽  
Analysis Techniques ◽  
Negative Sentiment ◽  
Public Health Strategies ◽  
The Impact

Background: Contents published on social media have an impact on individuals and on their decision making. Knowing the sentiment toward diabetes is fundamental to understanding the impact that such information could have on people affected with this health condition and their family members. The objective of this study is to analyze the sentiment expressed in messages on diabetes posted on Twitter. Method: Tweets including one of the terms “diabetes,” “t1d,” and/or “t2d” were extracted for one week using the Twitter standard API. Only the text message and the number of followers of the users were extracted. The sentiment analysis was performed by using SentiStrength. Results: A total of 67 421 tweets were automatically extracted, of those 3.7% specifically referred to T1D; and 6.8% specifically mentioned T2D. One or more emojis were included in 7.0% of the posts. Tweets specifically mentioning T2D and that did not include emojis were significantly more negative than the tweets that included emojis (–2.22 vs −1.48, P < .001). Tweets on T1D and that included emojis were both significantly more positive and also less negative than tweets without emojis (1.71 vs 1.49 and −1.31 vs −1.50, respectively; P < .005). The number of followers had a negative association with positive sentiment strength ( r = –.023, P < .001) and a positive association with negative sentiment ( r = .016, P < .001). Conclusion: The use of sentiment analysis techniques on social media could increase our knowledge of how social media impact people with diabetes and their families and could help to improve public health strategies.

Sentiment Analysis Techniques for Social Media-Based Recommendation Systems

2020 ◽  
Author(s):  
Selvi Munuswamy ◽  
M. S. Saranya ◽  
S. Ganapathy ◽  
S. Muthurajkumar ◽  
A. Kannan
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Recommendation Systems ◽  
Analysis Techniques

scholarly journals Deep Learning for Social Media Sentiment Analysis

MATEMATIKA ◽  
2020 ◽  
Vol 36 (2) ◽  
pp. 99-111
Author(s):  
Kartika Fithriasari ◽  
Saidah Zahrotul Jannah ◽  
Zakya Reyhana
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Characteristic Curve ◽  
Sentiment Classification ◽  
Radio Station ◽  
Learning Methods ◽  
Text Data ◽  
Media Sentiment

Social media is used as a tool by many people to express their opinions. Sentiment analysis for social media is very important, as it allows information to be obtained about public opinion on government performance. The goal of this research is to learn about the opinions of Surabaya citizens, using deep learning methods. The data are extracted from the official Twitter accounts of the Surabaya government and a private radio station in Surabaya. The data are grouped into two categories: positive and negative sentiments. This research is conducted in three steps: data pre-processing, sentiment classification, and visualization. Data pre-processing is required before modelling approaches are applied. It is used to transform the unstructured text data into structured data. The data pre-processing consists of case folding, tokenizing, and the removal of stop words. Deep learning methods are then applied to the data. A Backpropagation Neural Network (BNN) and a Convolutional Neural Network (CNN) are used to perform the sentiment classification. The BNN and CNN are compared using various metrics, such as precision, sensitivity, and area under the receiver operating characteristic curve (AUC). A word cloud is then used to visualize the data and find the most frequent words in each class. The results show that the sentiment classification with CNN is better than that with the BNN because the values for the precision, sensitivity and AUC are higher.

scholarly journals Ternary Classification of Product Based Reviews: Survey, Open Issues and New Approach for Sentiment Analysis

2021 ◽  
pp. 1-8
Author(s):  
Sushila Sonare ◽  
◽  
Dr. Megha Kamble ◽  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Text Data ◽  
New Approach ◽  
Meaningful Information ◽  
Multi Class Classification ◽  
Almost All ◽  
Open Issues

Now-a-days, it is very common that the customers share their thoughts about any product, brand and their experience in social media. The analysts collect these reviews and process it, to extract meaningful information about the product. The beauty of social media is, it’s involved in all the domains. So the analysts got reviews from different social media and platforms for almost all kind of thing. The Sentiment Analysis is applied to predict outcomes for getting useful information, for ex.; like predict the blockbuster for a movie, rating for any new launches and many more. This type of prediction is really helpful for the customer to buy any goods or take any services in this competitive world. This paper is focused on e-commerce website reviews which are normally in text form with some special characters and some symbols (emojis). Each word in this text set got some meaning in terms of context, emotion and prior experience. These characteristics contribute to some of the features of text data for prediction. The objective of this paper is to compile existing research works on text analysis and emotion based analysis. The open issues and challenges of document based sentiment analysis are also discussed. The paper concluded with proposing a new approach of multi class classification. Ternary classification for classes positive, negative and neutral is suggested primarily for product based text and emoji reviews on Twitter social media.

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