scholarly journals Real Time Opinion Mining and Analysis of Twitter Data

2018 ◽  
Vol 7 (3.12) ◽  
pp. 351
Author(s):  
K Senthil Kumar ◽  
Mohammad Musab Trumboo ◽  
Vaibhav . ◽  
Satyajai Ahlawat
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Far East ◽  
Lexical Database ◽  
Training Set ◽  
Twitter Data ◽  
The Far East ◽  
The People ◽  
Single Person ◽  
Mass Information

This era, in which we currently stand, is an era of public opinion and mass information. People from all around the globe are joined together through various information junctions to create a global community, where one thing from the far east reaches to the people of the far west within seconds. Nothing is hidden, everything and anything can be scrutinized to its core and through these global criticisms and mass discussions of gigantic magnitude, we have reached to the pinnacle of correct decisions and better choices. These pseudo social groups and data junctions have bombarded our society so much that they now hold the forelock of our opinions and sentiments, ergo, we reach out to these groups to achieve a better outcome. But, all this enormous data and all these opinions cannot be researched by a single person, hence, comes the need of sentiment analysis. In this paper we’ll try to accomplish this by creating a system that will enable us to fetch tweets from twitter and use those tweets against a lexical database which will create a training set and then compare it with the pre-fetched tweets. Through this we will be able to assign a polarity to all the tweets by means of which we can address them as negative, positive or neutral and this is the very foundation of sentiment analysis, so subtle yet so magnificent.  

scholarly journals An Approach To Twitter Sentiment Analysis Over Hadoop

2018 ◽  
Vol 7 (4.5) ◽  
pp. 374
Author(s):  
Yazala Ritika Siril Paul ◽  
Dilipkumar A. Borikar
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Emotional State ◽  
Streaming Data ◽  
Data Streaming ◽  
Text Data ◽  
Twitter Data ◽  
The People ◽  
Data Platform ◽  
Apache Hive

Sentiment analysis is the process of identifying people’s attitude and emotional state from the language they use via any social websites or other sources. The main aim is to identify a set of potential features in the review and extract the opinion expressions of those features by making full use of their associations. The Twitter has now become a routine for the people around the world to post thousands of reactions and opinions on every topic, every second of every single day. It’s like one big psychological database that’s constantly being updated and which can be used to analyze the sentiments of the people. Hadoop is one of the best options available for twitter data sentiment analysis and which also works for the distributed big data, streaming data, text data etc.  This paper provides an efficient mechanism to perform sentiment analysis/ opinion mining on Twitter data over Hortonworks Data platform, which provides Hadoop on Windows, with the assistance of Apache Flume, Apache HDFS and Apache Hive. 

scholarly journals Detection of Depression and Mental illness of Twitter users using Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1331-1335
Keyword(s):  
Sentiment Analysis ◽  
Real Time ◽  
Opinion Mining ◽  
Communication Tool ◽  
The Public ◽  
Twitter Data ◽  
The People ◽  
Document Frequency ◽  
Recent Emergence ◽  
Twitter Users

Today Micro-blogging has become a popular Internet-user communication tool. Millions of users exchange views on different aspects of their lives. Thus micro blogging websites are a rich source of opinion mining data or Sentiment Analysis (SA) information. Due to the recent emergence of micro blogging, there are a few research works devoted to this subject. We concentrate in our paper on Twitter, one of the prominent micro blogging sites to analyze sentiment of the public. We'll demonstrate, how to gather real-time twitter data for sentiment analysis or opinion mining purposes, and employed algorithms like Term Frequency - Inverse Document Frequency (TF-IDF), Bag of Words (BOW) and Multinomial Naive Bayes ( MNB). We are able to determine positive and negative sentiments for the real-time twitter data using the above chosen algorithms. Experimental evaluations below shows that the algorithms used are efficient and it can be used as a application in detection of the depression of the people. We worked with English in this article, but for any other language it can be used.

scholarly journals Employment issues in the modern Russia: five urgent matters

2017 ◽  
pp. 10-20
Author(s):  
A. G. Aganbegyan
Keyword(s):  
Labor Force ◽  
National Economy ◽  
Far East ◽  
Far East Of Russia ◽  
Unemployment Benefits ◽  
The Public ◽  
The Far East ◽  
The People

The employment issues existing in contemporary Russia including its socio-demographic, economic and regional dimensions are considered. It is argued and substantiated that priority strategies to cope with these issues include: reduction of unemployment and handling of the unemployment benefits’ payments; prevention of the labor force decreasing; including informal (unreported) employment into the public statistical accounting; providing for the people inflow to and increasing employment of those living in Siberia and the Far East of Russia; organization of 25 million high-productive jobs in the national economy.

scholarly journals A Survey on Analysis of Twitter Opinion Mining Using Sentiment Analysis

2019 ◽  
pp. 537-542
Author(s):  
Vishnu VardanReddy ◽  
Mahesh Maila ◽  
Sai Sri Raghava ◽  
Yashwanth Avvaru ◽  
Sri. V. Koteswarao
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Online Communication ◽  
Valuable Insight ◽  
Huge Amount ◽  
Twitter Data ◽  
Product Service ◽  
Different Types

In recent years, there is a rapid growth in online communication. There are many social networking sites and related mobile applications, and some more are still emerging. Huge amount of data is generated by these sites everyday and this data can be used as a source for various analysis purposes. Twitter is one of the most popular networking sites with millions of users. There are users with different views and varieties of reviews in the form of tweets are generated by them. Nowadays Opinion Mining has become an emerging topic of research due to lot of opinionated data available on Blogs & social networking sites. Tracking different types of opinions & summarizing them can provide valuable insight to different types of opinions to users who use Social networking sites to get reviews about any product, service or any topic. Analysis of opinions & its classification on the basis of polarity (positive, negative, neutral) is a challenging task. Lot of work has been done on sentiment analysis of twitter data and lot needs to be done. In this paper we discuss the levels, approaches of sentiment analysis, sentiment analysis of twitter data, existing tools available for sentiment analysis and the steps involved for same. Two approaches are discussed with an example which works on machine learning and lexicon based respectively.

Taxonomies of Yellow

2011 ◽  
Author(s):  
Michael Keevak
Keyword(s):  
Eighteenth Century ◽  
Homo Sapiens ◽  
Comparative Anatomy ◽  
Far East ◽  
East Asians ◽  
The Earth ◽  
The Far East ◽  
The People ◽  
Carl Linnaeus ◽  
French Physician

This chapter focuses on the emergence of new sorts of human taxonomies as well as new claims about the color of all human groups, including East Asians, during the course of the eighteenth century, as well as their racial implications. It first considers the theory advanced in 1684 by the French physician and traveler François Bernier, who proposed a “new division of the Earth, according to the different species or races of man which inhabit it.” One of these races, he suggested, was yellow. Then in 1735, the Swedish botanist Carl Linnaeus published Systema naturae, in which he categorized homo sapiens into four different skin colors. Finally, at the end of the eighteenth century, Johann Friedrich Blumenbach, also a physician and the founder of comparative anatomy, declared that the people of the Far East were a yellow race, as distinct from the white “Caucasian” one.

Application of Sentiment Analysis in Movie reviews

2019 ◽  
pp. 77-90 ◽  
Author(s):  
ThippaReddy Gadekallu ◽  
Akshat Soni ◽  
Deeptanu Sarkar ◽  
Lakshmanna Kuruva
Keyword(s):  
Sentiment Analysis ◽  
Experimental Work ◽  
Opinion Mining ◽  
Rating Systems ◽  
Domain Specific ◽  
Analysis Process ◽  
The People ◽  
Textual Data ◽  
Feature Based ◽  
The Web

Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic from a structured, semi-structured, or unstructured textual data. In this chapter, the authors try to focus the task of sentiment analysis on IMDB movie review database. This chapter presents the experimental work on a new kind of domain-specific feature-based heuristic for aspect-level sentiment analysis of movie reviews. The authors have devised an aspect-oriented scheme that analyzes the textual reviews of a movie and assign it a sentiment label on each aspect. Finally, the authors conclude that incorporating syntactical information in the models is vital to the sentiment analysis process. The authors also conclude that the proposed approach to sentiment classification supplements the existing rating movie rating systems used across the web and will serve as base to future researches in this domain.

Opinion mining and sentiment analysis on a Twitter data stream

2012 ◽  
Author(s):  
Balakrishnan Gokulakrishnan ◽  
Pavalanathan Priyanthan ◽  
Thiruchittampalam Ragavan ◽  
Nadarajah Prasath ◽  
AShehan Perera
Keyword(s):  
Sentiment Analysis ◽  
Data Stream ◽  
Opinion Mining ◽  
Twitter Data

scholarly journals Mythological context of folk etymology: hydronyms and oronyms in the Khakass non-fairy prose

2020 ◽  
pp. 29-40
Author(s):  
V. V. Mindibekova ◽  
Keyword(s):  
Far East ◽  
Linguistic Features ◽  
White River ◽  
The Far East ◽  
The People ◽  
Unique Material ◽  
Rivers And Lakes ◽  
Specific Meaning ◽  
The Rich ◽  
First Time

The author analyzes the main types of plots of toponymic legends that have become wide-spread among the Khakass people and are of artistic and historical value. The toponymic space of the Khakass non-fairy prose is considered for the first time. Of particular interest are the toponymic legends about rivers and lakes. The toponymic legends about the mountains are no less diverse in their composition. Stories explaining the origin of the names of various ob-jects in the area play a significant role in the non-fairy prose. The research is based on the ma-terial of the volume “Khakas non-fairy prose” of the academic series “Monuments of Folklore of the Peoples of Siberia and the Far East” (2016). The study has identified the genre, textological and linguistic features of toponymic legends. Toponyms reflect the geographical features of the area. The legends contain terms reflecting flora and fauna of the steppe area and the rich world of nature. The image plays an important role in characterizing the topo-nyms and distinguishing between natural objects (the rivers Кim “Yenisei,” Agban “Abakan,” Ah Uus “White River,” Khara Uus “Black River,” Saraa adai kol “Lake of the Yellow Dog”). Toponyms can also include numbers with a specific meaning. Toponymic legends are consid-ered to be one of the most important sources for studying the material and spiritual culture of the people. Folklore toponyms are extremely rich and unique material, which can be used to investigate the toponymic system of the non-fairy prose of the Khakass people.

scholarly journals The Russian China in the Last D. A. Prigov’s Novel

2020 ◽  
Vol 15 (1) ◽  
pp. 146-165
Author(s):  
E. V. Kapinos
Keyword(s):  
Far East ◽  
Main Task ◽  
The Novel ◽  
Main Character ◽  
The Far East ◽  
The People ◽  
The Gift ◽  
Pass Through ◽  
John Wilkins

The article relates to the last novel by D. A. Prigov “Katya the Chinese” (published in 2007), based on the memories of the writer’s wife, N. G. Burova from Harbin, who was born and grew up in the Russian China and left it in 1950s. The main task of the article is to show what stylistic techniques, plots, motives, subtexts allow to recreate the atmosphere of the Russian China and the generalized image of the East. The narration synthesizes the memories of the people of Harbin and recognizable storylines of D. A. Prigov’s work (such, for example, as a fantastic bestiary). In the subtext of the novel the following works are found: feuilleton by I. Ilf and E. Petrov “Nikudykin the Commtied”, pro- totyped by the “futurist of life” V. Goltsshmidt, who traveled in 1918–1920s with lectures on Siberia and the Far East, V. Nabokov’s novel “The Gift” with his father’s Asian journey (the plot about his father in “The Gift” influenced the plot about the girl’s father in “Katya the Chinese”), the “Chinese” stories by J. L. Borges “The Garden of Forking Paths” and “The Analytical language of John Wilkins”, etc. In Prigov’s narrative, a particular role is played by an autobiographical excerpt about the Tashkent artist A. N. Volkov and an insertion novel about the “Monastery of Flying Cats”, in- tentionally inaccurately stylized by Prigov as a Chinese legend. Many motifs and subtexts of the novel pass through the prism of the child’s consciousness (the “girl’s”, who is the main character of the novel), which gives the image of the Russian East, along with documentary, fantastic features.

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