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 Twitter data analysis using hadoop ecosystems and apache zeppelin

2019 ◽  
Vol 16 (3) ◽  
pp. 1490
Author(s):  
Stanly Wilson ◽  
Sivakumar R
Keyword(s):  
Streaming Data ◽  
Data Streaming ◽  
Text Data ◽  
Twitter Data ◽  
The People ◽  
Textual Data ◽  
Hadoop Distributed File System ◽  
Twitter Data Analysis ◽  
Apache Pig ◽  
Small Industries

The day-to-day life of the people doesn't depend only on what they think, but it is affected and influenced by what others think. The advertisements and campaigns of the favourite celebrities and mesmerizing personalities influence the way people think and see the world. People get the news and information at lightning speed than ever before. The growth of textual data on the internet is very fast. People express themselves in various ways on the web every minute. They make use of various platforms to share their views and opinions. A huge amount of data is being generated at every moment on this process. Being one of the most important and well-known social media of the present time, millions of tweets are posted on Twitter every day. These tweets are a source of very important information and it can be made use for business, small industries, creating government policies, and various studies can be performed by using it. This paper focuses on the location from where the tweets are posted and the language in which the tweets are written. These details can be effectively analysed by using Hadoop. Hadoop is a tool that is used to analyze distributed big data, streaming data, timestamp data and text data. With the help of Apache Flume, the tweets can be collected from Twitter and then sink in the HDFS (Hadoop Distributed File System). These raw data then analyzed using Apache Pig and the information available can be made use for social and commercial purposes. The result will be visualized using Apache Zeppelin.

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 Designing Framework for Real Time Twitter Data Analytics using Apache Flume and Pig

2020 ◽  
Vol 8 (6) ◽  
pp. 4474-4477
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Social Issues ◽  
Analytical Framework ◽  
Streaming Data ◽  
Data Streaming ◽  
Text Data ◽  
Breaking News ◽  
Unstructured Text ◽  
Twitter Data

In the world of technology, people prefer social media to express themselves. Record says Twitter has more than 321 million active users with 100 million users posting approximately 340 million tweets a day. Twitter is the largest source of breaking news on social issues specially election-related where people can express their views also suggest their opinion. Twitter is generating unlimited unstructured text data. Hadoop is one of the finest tools accessible for analyzing twitter data because it supports processing of distributed big data, streaming data, time stamped data, text data etc. Whereas Apache Flume is used to extract real time twitter data into HDFS. This study attempts to establish an analytical framework to derive and interpret structured as well as unstructured Twitter data. The proposed framework comprises of real time twitter data insertion, its processing, and data visualization utilizing Apache Flume and pig. In this project we fetch positive and negative tweets on election data from twitter and analyzing the party status and the probability to win the election.

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 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.

scholarly journals Opinion Mining and Sentiment Analysis on Big Data

2020 ◽  
pp. 410-415
Author(s):  
Shruti Rajkumar Choudhary
Keyword(s):  
Artificial Intelligence ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
Text Data ◽  
The Public ◽  
Public Sentiment

<p>Opinion mining is extract subjective information from text data using tools such as NLP, text analysis etc. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product.In this project the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in terms of positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.</p>

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.

Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis

2018 ◽  
Vol 13 (3) ◽  
pp. 99-111 ◽  
Author(s):  
Srinidhi Hiriyannaiah ◽  
G.M. Siddesh ◽  
K.G. Srinivasa
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Streaming Data ◽  
Classification Method ◽  
Emotion Classification ◽  
Knowledge Based ◽  
Twitter Data ◽  
Internet Users ◽  
Word Classification ◽  
Three Stages

This article describes how recent advances in computing have led to an increase in the generation of data in fields such as social media, medical, power and others. With the rapid increase in internet users, social media has given power for sentiment analysis or opinion mining. It is a highly challenging task for storing, querying and analyzing such types of data. This article aims at providing a solution to store, query and analyze streaming data using Apache Kafka as the platform and twitter data as an example for analysis. A three-way classification method is proposed for sentimental analysis of twitter data that combines both the approaches for knowledge-based and machine-learning using three stages namely emotion classification, word classification and sentiment classification. The hybrid three-way classification approach was evaluated using a sample of five query strings on twitter and compared with existing emotion classifier, polarity classifier and Naïve Bayes classifier for sentimental analysis. The accuracy of the results of the proposed approach is superior when compared to existing approaches.

Sentiment Analysis of Twitter Data to Examine the Movement of Exchange Rate and Sensex

2020 ◽  
Vol 17 (8) ◽  
pp. 3323-3327
Author(s):  
N. Chethan ◽  
R. Sangeetha
Keyword(s):  
Exchange Rate ◽  
Sentiment Analysis ◽  
Stock Price ◽  
Text Data ◽  
Price Movement ◽  
Word Cloud ◽  
Twitter Data ◽  
R Programming ◽  
Sentiment Score ◽  
Key Events

In this paper tweets available on social media about USD/INR exchange rate, BSE Sensex, NSE Nifty have been collected and Sentiment Analysis using R programming has been performed. A sentiment score has been obtained for each of the sentences and also word cloud plot have been obtained. In this paper twitter feeds are collected using the keywords: USD/INR, #USD/INR, #BSE, #Sensex, #NSE. For the purpose of obtaining the tweets, R programming is used. In this study to obtain the word cloud plot, the sentiment has been classified across 8 categories viz Anticipation, anger, trust, surprise, sadness, joy, fear and disgust. On a day to day basis, Sentiment Analysis gives the overall sentiment on a given day stating if the sentiment for a given day is either Positive or Negative or whether it is Neutral. It also breaks down the tweets into various categories which help in identifying the moods of the investors not only by the sentiment but also by the number of tweets. Further, the word cloud plot offers a simple and effective way of capturing the key events or news which was discussed on Twitter. Sentiment analysis can be used effectively by investors to make a prediction of what direction the stock price movements will happen based on the sentiment prevailing in the market. This study also shows how R programming can be used to perform sentiment analysis on the stock price movement based on twitter feeds. Word cloud can be used to visualize text data in which the size of each word cloud denotes its significance.

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
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