With microblogging platforms such as Twitter generating
huge amounts of textual data every day, the possibilities of
knowledge discovery through Twitter data becomes
increasingly relevant. Similar to the public voting mechanism
on websites such as the Internet Movie Database (IMDb) that
aggregates movies ratings, Twitter content contains
reflections of public opinion about movies. This study aims to
explore the use of Twitter content as textual data for
predicting the movie rating. In this study, we extract number
of tweets and compiled to predict the rating scores of newly
released movies. Predictions were done with the algorithms,
exploring the tweet polarity. In addition, this study explores
the use of several different kinds of tweet classification
Algorithm and movie rating algorithm. Results show that
movie rating developed by our application is compared to
IMDB and Rotten Tomatoes.
This is small twitter sentiment analysis project which will take one keyword(which is the primary way of storing the tweet in Twitter) and number of tweets, and gives you the pictorial representation of the overall sentiment.
Trend detection and analysis on TwitterLukas Masuch
By Henning Muszynski, Benjamin Räthlein & Lukas Masuch
The popularity of social media services has increased exponentially in the last few years. The combination of big social data and powerful analytical technologies makes it possible to gain highly valuable insights that otherwise might not be accessible. The Twitter Analyzer comprises several components to collect, analyze and visualize Twitter data. Therefore, we explored various related technologies to implement this tool. We collected about 38 million english tweets related to various and analyzed those data with machine learning techniques to compute the respective sentiment and detect common topics. Furthermore, we visualized the results using varying visualization techniques to emphasize different aspects such as a wordcloud, several chart-types and geospatial visualizations. Used technologies: MongoDB, Python, Twython, Python NLTK, wordcloud2.js, wordfreq, amCharts, Google BigQuery, Google Cloud Storage, CartoDB, EtcML.
Project Report for Twitter Sentiment Analysis done using Apache Flume and data is analysed using Hive.
I intend to address the following questions:
How raw tweets can be used to find audience’s perception or sentiment about a person ?
How Hadoop can be used to solve this problem?
How Apache Hive can be used to organize the final data in a tabular format and query it?
How a data visualization tool can be used to display the findings?
Sentiment analysis of Twitter data using pythonHetu Bhavsar
Twitter is a popular social networking website where users posts and interact with messages known as “tweets”. To automate the analysis of such data, the area of Sentiment Analysis has emerged. It aims at identifying opinionative data in the Web and classifying them according to their polarity, i.e., whether they carry a positive or negative connotation. We will attempt to conduct sentiment analysis on “tweets” using various different machine learning algorithms.
Sentiment Analysis of Twitter tweets using supervised classification technique IJERA Editor
Making use of social media for analyzing the perceptions of the masses over a product, event or a person has
gained momentum in recent times. Out of a wide array of social networks, we chose Twitter for our analysis as
the opinions expressed their, are concise and bear a distinctive polarity. Here, we collect the most recent tweets
on users' area of interest and analyze them. The extracted tweets are then segregated as positive, negative and
neutral. We do the classification in following manner: collect the tweets using Twitter API; then we process the
collected tweets to convert all letters to lowercase, eliminate special characters etc. which makes the
classification more efficient; the processed tweets are classified using a supervised classification technique. We
make use of Naive Bayes classifier to segregate the tweets as positive, negative and neutral. We use a set of
sample tweets to train the classifier. The percentage of the tweets in each category is then computed and the
result is represented graphically. The result can be used further to gain an insight into the views of the people
using Twitter about a particular topic that is being searched by the user. It can help corporate houses devise
strategies on the basis of the popularity of their product among the masses. It may help the consumers to make
informed choices based on the general sentiment expressed by the Twitter users on a product.
This is small twitter sentiment analysis project which will take one keyword(which is the primary way of storing the tweet in Twitter) and number of tweets, and gives you the pictorial representation of the overall sentiment.
Trend detection and analysis on TwitterLukas Masuch
By Henning Muszynski, Benjamin Räthlein & Lukas Masuch
The popularity of social media services has increased exponentially in the last few years. The combination of big social data and powerful analytical technologies makes it possible to gain highly valuable insights that otherwise might not be accessible. The Twitter Analyzer comprises several components to collect, analyze and visualize Twitter data. Therefore, we explored various related technologies to implement this tool. We collected about 38 million english tweets related to various and analyzed those data with machine learning techniques to compute the respective sentiment and detect common topics. Furthermore, we visualized the results using varying visualization techniques to emphasize different aspects such as a wordcloud, several chart-types and geospatial visualizations. Used technologies: MongoDB, Python, Twython, Python NLTK, wordcloud2.js, wordfreq, amCharts, Google BigQuery, Google Cloud Storage, CartoDB, EtcML.
Project Report for Twitter Sentiment Analysis done using Apache Flume and data is analysed using Hive.
I intend to address the following questions:
How raw tweets can be used to find audience’s perception or sentiment about a person ?
How Hadoop can be used to solve this problem?
How Apache Hive can be used to organize the final data in a tabular format and query it?
How a data visualization tool can be used to display the findings?
Sentiment analysis of Twitter data using pythonHetu Bhavsar
Twitter is a popular social networking website where users posts and interact with messages known as “tweets”. To automate the analysis of such data, the area of Sentiment Analysis has emerged. It aims at identifying opinionative data in the Web and classifying them according to their polarity, i.e., whether they carry a positive or negative connotation. We will attempt to conduct sentiment analysis on “tweets” using various different machine learning algorithms.
Sentiment Analysis of Twitter tweets using supervised classification technique IJERA Editor
Making use of social media for analyzing the perceptions of the masses over a product, event or a person has
gained momentum in recent times. Out of a wide array of social networks, we chose Twitter for our analysis as
the opinions expressed their, are concise and bear a distinctive polarity. Here, we collect the most recent tweets
on users' area of interest and analyze them. The extracted tweets are then segregated as positive, negative and
neutral. We do the classification in following manner: collect the tweets using Twitter API; then we process the
collected tweets to convert all letters to lowercase, eliminate special characters etc. which makes the
classification more efficient; the processed tweets are classified using a supervised classification technique. We
make use of Naive Bayes classifier to segregate the tweets as positive, negative and neutral. We use a set of
sample tweets to train the classifier. The percentage of the tweets in each category is then computed and the
result is represented graphically. The result can be used further to gain an insight into the views of the people
using Twitter about a particular topic that is being searched by the user. It can help corporate houses devise
strategies on the basis of the popularity of their product among the masses. It may help the consumers to make
informed choices based on the general sentiment expressed by the Twitter users on a product.
Cloud Technologies providing Complete Solution for all
AcademicProjects Final Year/Semester Student Projects
For More Details,
Contact:
Mobile:- +91 8121953811,
whatsapp:- +91 8522991105,
Office:- 040-66411811
Email ID: cloudtechnologiesprojects@gmail.com
Sentiment analysis in twitter using python
Sentiment Analysis of tweets which are extracted using twitter API and applying various filters according to the use . The sentiment analysis is done using the Afinn dictionary which is a dictionary consisting of words with their corresponding rating. A rating between +5 and -5 . A positive rating is indicated a positive statement and a negative rating indicated a negative one and a rating of 0 indicates a neutral statement.
Twitter Sentiment Analysis Project Done using R.
In these Project we deal with the tweets database that are avaialble to us by the Twitter. We clean the tweets and break them out into tokens and than analysis each word using Bag of Word concept and than rate each word on the basis of the score wheter it is positive, negative and neutral.
We used Naive Baye's Classifier as our base.
Social Sensor for Real Time Event DetectionIJERA Editor
Social sites play an important role in our day to day life. Peoples are spending at most of the time on social
networking sites and for this reason social networking sites have received a more attention in last few years.
Social networking websites have received greater attention in the past few years .Twitter is one of the most
popular networking site which received attention from peoples by providing its real time nature. The important
characteristic of our system is its real-time nature. We create a system to collects the tweets from various groups
of users and detect real time events like earthquake, traffic etc. To detect real time events, we build a classifier
of comments based on features such as the keywords in a comments, the number of words. With the help of
comments we will approximately find out the location of the target event. We assume each user as a sensor and
apply priority base algorithm which is used for tweet analysis.
To resolve the traffic congestion problem, we have to consider the volume of the traffic, traffic speed, road
occupancy etc. Classify tweets into a positive and negative class. Produce a probabilistic model for event
detection. We create group of users on the basis of their interest such as peoples from traffic police department
are post the tweets related to traffic event, also peoples form whether department are post their tweets related to
flood etc. Each tweet is associated with user group, tweet, time and location. By processing time and location
information, we can detect the real time events.
https://www.youtube.com/watch?v=nvlHJgRE3pU
Won ITAC Graduation Projects Competition, ITAC ID: GP2015.R10.75
A web application that analyze big volumes of product reviews, social networks posts and tweets related to a given product. Then, present these results of this big data analytical job in a user friendly, understandable, and easily interpreted manner that can be used by different customers for different purposes.
Technologies used:
1- Hadoop
2- Hadoop Streaming
3- R Statistical
4- PHP
5- Google Charts API
Make a query regarding a topic of interest and come to know the sentiment for the day in pie-chart or for the week in form of line-chart for the tweets gathered from twitter.com
SentiTweet is a sentiment analysis tool for identifying the sentiment of the tweets as positive, negative and neutral.SentiTweet comes to rescue to find the sentiment of a single tweet or a set of tweets. Not only that it also enables you to find out the sentiment of the entire tweet or specific phrases of the tweet.
Cloud Technologies providing Complete Solution for all
AcademicProjects Final Year/Semester Student Projects
For More Details,
Contact:
Mobile:- +91 8121953811,
whatsapp:- +91 8522991105,
Office:- 040-66411811
Email ID: cloudtechnologiesprojects@gmail.com
Sentiment analysis in twitter using python
Sentiment Analysis of tweets which are extracted using twitter API and applying various filters according to the use . The sentiment analysis is done using the Afinn dictionary which is a dictionary consisting of words with their corresponding rating. A rating between +5 and -5 . A positive rating is indicated a positive statement and a negative rating indicated a negative one and a rating of 0 indicates a neutral statement.
Twitter Sentiment Analysis Project Done using R.
In these Project we deal with the tweets database that are avaialble to us by the Twitter. We clean the tweets and break them out into tokens and than analysis each word using Bag of Word concept and than rate each word on the basis of the score wheter it is positive, negative and neutral.
We used Naive Baye's Classifier as our base.
Social Sensor for Real Time Event DetectionIJERA Editor
Social sites play an important role in our day to day life. Peoples are spending at most of the time on social
networking sites and for this reason social networking sites have received a more attention in last few years.
Social networking websites have received greater attention in the past few years .Twitter is one of the most
popular networking site which received attention from peoples by providing its real time nature. The important
characteristic of our system is its real-time nature. We create a system to collects the tweets from various groups
of users and detect real time events like earthquake, traffic etc. To detect real time events, we build a classifier
of comments based on features such as the keywords in a comments, the number of words. With the help of
comments we will approximately find out the location of the target event. We assume each user as a sensor and
apply priority base algorithm which is used for tweet analysis.
To resolve the traffic congestion problem, we have to consider the volume of the traffic, traffic speed, road
occupancy etc. Classify tweets into a positive and negative class. Produce a probabilistic model for event
detection. We create group of users on the basis of their interest such as peoples from traffic police department
are post the tweets related to traffic event, also peoples form whether department are post their tweets related to
flood etc. Each tweet is associated with user group, tweet, time and location. By processing time and location
information, we can detect the real time events.
https://www.youtube.com/watch?v=nvlHJgRE3pU
Won ITAC Graduation Projects Competition, ITAC ID: GP2015.R10.75
A web application that analyze big volumes of product reviews, social networks posts and tweets related to a given product. Then, present these results of this big data analytical job in a user friendly, understandable, and easily interpreted manner that can be used by different customers for different purposes.
Technologies used:
1- Hadoop
2- Hadoop Streaming
3- R Statistical
4- PHP
5- Google Charts API
Make a query regarding a topic of interest and come to know the sentiment for the day in pie-chart or for the week in form of line-chart for the tweets gathered from twitter.com
SentiTweet is a sentiment analysis tool for identifying the sentiment of the tweets as positive, negative and neutral.SentiTweet comes to rescue to find the sentiment of a single tweet or a set of tweets. Not only that it also enables you to find out the sentiment of the entire tweet or specific phrases of the tweet.
This is small twitter sentiment analysis project which will take one keyword(which is the primary way of storing the tweet in Twitter) and number of tweets, and gives you the pictorial representation of the overall sentiment.
What Is Sentiment Analysis?
Problem Statement
Why Twitter data?
The Process at a Glance
Methodology: How are we doing it?
Pre-processing of the datasets
Extract the candidate or take it as user input.
Calculate sentiment
Visualizing the candidate data
What visualization are we talking about?
Real time sentiment analysis of twitter feeds with the NASDAQ indexEric Tham
We do a real-time analysis on twitter feeds computing its sentiment analysis using the hash tag #NASDAQ. This sentiment index is found to correlate well with the hourly movements of the NASDAQ index over the period 14-17th Apr 2014. In particular, a Granger causality analysis shows that the hourly movements of the NASDAQ drives tweet sentiment real-time and not vice versa during this period.
Performance of Wideband Mobile Channel with Perfect Synchronism BPSK vs QPSK ...Editor Jacotech
Direct-sequence code-division multiple access (DS-CDMA) is
currently the subject of much research as it is a promising
multiple access capability for third and fourth generations
mobile communication systems. The synchronous DS-CDMA
system is well known for eliminating the effects of multiple
access interference (MAI) which limits the capacity and
degrades the BER performance of the system. In this paper,
we investigate the bit error rate (BER) performance of a
synchronous DS-CDMA system over a wideband mobile
radio channel. The BER performance is affected by the
difference in path length ΔL and the number of arriving
signals N. Furthermore, the effect of these parameters is
examined on the synchronous DS-CDMA system for different
users’ number as well as different processing gain Gp. In this
environment and under the above conditions the performances
of the BPSK (Binary Phase Shift Keying) and the QPSK
(Quadrature Phase Shift Keying) modulations are compared.
The promising simulation results showed the possibility of
applying this system to the wideband mobile radio channel.
Non integer order controller based robust performance analysis of a conical t...Editor Jacotech
The design of robust controller for any non linear process is a
challenging task because of the presence of various types of
uncertainties. In this paper, various design methods of robust
PID controller for the level control of conical tank are
discussed. Uncertainties are of different types, among that
structured uncertainty of 30% is introduced to the nominal
plant for analysing the robustness. As a first step, the control
of level is done by using conventional integer order controller
for both nominal and uncertain system. Then, the control is
done by means of Fractional Order Proportional Integral
Derivative (FOPID) controller for achieving robustness. With
the help of time series parameters, a comparison is made
between conventional PID and FOPID with respect to the
simulated output using MATLAB and also analyzed the
robustness.
FACTORS CAUSING STRESS AMONG FEMALE DOCTORS (A COMPARATIVE STUDY BETWEEN SELE...Editor Jacotech
It is an important task of working women to handle two
important tasks. Balancing these two roles at home and
work is very challenging task and causes stress at different
levels. Different dimension of working women’s life
involves in evolving the stress in working women’s life.
These stresses cause the imbalance at the front of and
handling family responsibility. In the current scenario,
doctors face many stressors that are peculiar to the medical
profession and doctors are required to have more
competencies than before in diagnosis ongoing
management of medical conditions. This means increased
responsibilities which may contribute to stress. Stress
experienced at work can have adverse outcomes for the
well-being of individual employees and organization as
whole. My study is focusing on identifying the factors
causing stress among female doctors working for public
and private hospitals and their stress levels associations
with respect to sector. A sample of 300 female doctors
from urban area participated in this study. Out of this, 150
each are from public and private hospitals respectively. A
self-made standardized tool was administered based on five
point scale. Results indicates that the values were found to
be 0.000 in all the cases except, psychosomatic problems
(0.004) which is lesser than (0.05) p-value resulting into
rejection of null hypotheses , consequently revealing an
association between sector of female doctors and stress due
to workload, working condition, physical exertion,
emotional exhaustion, job security, organizational support,
work family conflict, family adjustment, task demands,
psychosomatic problems, patient’s expectation and working
hours.
ANALYSIS AND DESIGN OF MULTIPLE WATERMARKING IN A VIDEO FOR AUTHENTICATION AN...Editor Jacotech
Watermarking technique be employ instance & for a second time for
validation and protection of digital data (images, video and audio
files, digital repositories and libraries, web publishing). It is helpful
to copyright protection and illegal copying of digital data like video
frames and making digital data more robust and imperceptible. With
the advent of internet, creation and delivery of digital data has grown
many fold. In that Scenario has to need a technique for transferring
digital data securely without changing their originality and
robustness. In this paper proposed a plan of latest watermarking
method which involves inserting and adding two or more digital data
or pictures in a single video frame for the principle of protection and
replicate the similar procedure for N no video frames for
authentication of entire digital video. After that digital video is
encrypted and decrypted by using motion vector bit-xor encryption
and decryption technique.
The Impact of Line Resistance on the Performance of Controllable Series Compe...Editor Jacotech
In recent years controllable FACTS devices are increasingly
integrated into the transmission system. FACTS devices that
provide series control such as Controllable Series Compensator
(CSC) has significant effect on the voltage stability of Electric
Power system. In this work impact of line resistance on the
performance of CSC in a single-load infinitive-bus (SLIB)
model is investigated. The proposed framework is applied to
SLIB model and obtained results demonstrates that line
resistance has considerable effect on voltage stability limits and
performance of CSC.
Security Strength Evaluation of Some Chaos Based Substitution-BoxesEditor Jacotech
Recently, handful amount of S-boxes, using the various
methods such as affine transformations, gray coding,
optimization, chaotic systems, etc, have been suggested. It is
prudent to use cryptographically strong S-boxes for the design
of powerful ciphers. In this paper, we sampled some widely
used 8×8 S-boxes which are recently synthesized and security
analysis and evaluation is executed to uncover the best
candidate(s). The performance analysis is exercised against
the crucial measures like nonlinearity, linear approximation
probability, algebraic immunity, algebraic complexity,
differential uniformity. These parameters are custom selected
because their scores decide the security strength against
cryptographic assaults like linear cryptanalysis, algebraic
attacks, and differential cryptanalysis. The anticipated
analysis in this work facilitates the cryptographers, designers,
researchers to choose suitable candidate decided over many
parameters and can be engaged in modern block encryption
systems that solely rely on 8×8 S-box. Moreover, the analysis
assists in articulating efficient S-boxes and to evaluate the
attacks resistivity of their S-boxes.
Traffic Detection System is an Android application that aims at determining the behavior of traffic in a particular location. It calculates the speed of the vehicle and the level of congestion or the amount of traffic is determined on the basis of the values of sensors. If any such obstruct found, then the driver is provided an option to send messages regarding high traffic to his/her friends. After a distinct number of repeated low speed and breaks, the location of the vehicle (latitude and longitude) send to a pre-specified contact (selected in case of traffic congestion) through an SMS. This application uses the features of the Global positioning system. The Latitude, as well as the longitude of the location where traffic jams are formed, is sent to the friends of the user. The Goggle map of the location also sends to the friends. It uses the SMS Manager a functionality of Android. The friends receiving the messages will thereby avoid taking the congested route and hence the level of traffic on the congested road will decrease, and the friends will reach the destination in comparatively less time.
Performance analysis of aodv with the constraints of varying terrain area and...Editor Jacotech
Mobile Ad Hoc Networks (MANETs) are wireless networks,
where there is no requirement for any infrastructure support to
transfer data packets between mobile nodes. These nodes
communicate in a multi-hop mode; each mobile node acts
both as a host and router. The main job of Quality of Service
(QoS)[1][2] routing in MANETs is to search and establish
routes among different mobile nodes for satisfying QoS
requirements of wireless sensor networks as PDR, Average
end-to-end delay, Average Throughput. The QoS routing
protocols efficient for commercial, real-time and multimedia
applications are in demand for day to day activities[2].
Modeling of solar array and analyze the current transient response of shunt s...Editor Jacotech
Spacecraft bus voltage is regulated by power
conditioning unit using switching shunt voltage regulator having
solar array cells as the primary source of power. This source
switches between the bus loads and the shunt switch for fine
control of spacecraft bus voltage. The effect of solar array cell
capacitance [5][6] along with inductance and resistance of the
interface wires between solar cells and power conditioning
unit[1], generates damped sinusoidal currents superimposed on
the short circuit current of solar cell when shunted through
switch. The peak current stress on the shunt switch is to be
considered in the selection of shunt switch in power conditioning
unit. The analysis of current transients of shunt switch in PCU
considering actual spacecraft interface wire length by
illumination of solar panel (combination of series and parallel
solar cells) is difficult with hardware simulation. Software
simulation by modeling solar cell is carried out for a single string
(one parallel) in Pspice [6]. Since in spacecrafts number of
parallels and interface cable length are variable parameters the
analysis of current transients of shunt switch is carried out by
modeling solar array with the help of solar cell model[6] for the
actual spacecraft condition.
License plate recognition an insight to the proposed approach for plate local...Editor Jacotech
License Plate Recognition (LPR) system for vehicles is an innovative and a very challenging area for research due to the innumerous plate formats and the nonuniform outdoor illumination conditions during which images are acquired. Thus, most approaches developed, work under certain restrictions such as fixed illumination, stationary background and limited speed. Algorithms developed for LPR systems are generally composed of three significant stages: 1] localization of the license plate from an entire scene image; 2] segmentation of the characters on the plate; 3] recognition of each of the segmented characters. A simple approach for preprocessing of the images, localization and extraction phase has been described in this paper. Numerous procedures have been developed for LPR systems and are assessed in this paper taking into consideration issues like processing time, computational power and recognition rate wherever available.
Design of airfoil using backpropagation training with mixed approachEditor Jacotech
Levenberg-Marquardt back-propagation training method has some limitations associated with over fitting and local optimum problems. Here, we proposed a new algorithm to increase the convergence speed of Backpropagation learning to design the airfoil. The aerodynamic force coefficients corresponding to series of airfoil are stored in a database along with the airfoil coordinates. A feedforward neural network is created with aerodynamic coefficient as input to produce the airfoil coordinates as output. In the proposed algorithm, for output layer, we used the cost function having linear & nonlinear error terms then for the hidden layer, we used steepest descent cost function. Results indicate that this mixed approach greatly enhances the training of artificial neural network and may accurately predict airfoil profile.
Ant colony optimization based routing algorithm in various wireless sensor ne...Editor Jacotech
Wireless Sensor Network has several issues and challenges due to limited battery backup, limited computation capability, and limited computation capability. These issues and challenges must be taken care while designing the algorithms to increase the Network lifetime of WSN. Routing, the act of moving information across an internet world from a source to a destination is one of the vital issue associated with Wireless Sensor Network. The Ant Colony Optimization (ACO) algorithm is a probabilistic technique for solving computational problems that can be used to find optimal paths through graphs. The short route will be increasingly enhanced therefore become more attractive. The foraging behavior and optimal route finding capability of ants can be the inspiration for ACO based algorithm in WSN. The nature of ants is to wander randomly in search of food from their nest. While moving, ants lay down a pheromone trail on the ground. This chemical pheromone has the ability to evaporate with the time. Ants have the ability to smell pheromone. When selecting their path, they tend to select, probably the paths that has strong pheromone concentrations. As soon as an ant finds a food source, carries some of it back to the nest. While returning, the quantity of chemical pheromone that an ant lay down on the ground may depend on the quantity and quality of the food. The pheromone trails will lead other ants towards the food source. The path which has the strongest pheromone concentration is followed by the ant which is the shortest paths between their nest and food source. This paper surveys the ACO based routing in various Networking domains like Wireless Sensor Networks and Mobile Ad Hoc Networks.
An efficient ant optimized multipath routing in wireless sensor networkEditor Jacotech
Today, the Wireless Sensor Network is increasingly gaining popularity and importance. It is the more interesting and stimulating area of research. Now, the WSN is applied in object tracking and environmental monitoring applications. This paper presents the self-optimized model of multipath routing algorithm for WSN which considers definite parameters like delay, throughput level and loss and generates the outcomes that maximizes data throughput rate and minimizes delay and loss. This algorithm is based on ANT optimization technique that will bring out an optimal and organized route for WSN and is also to avoid congestion in WSN, the algorithm incorporate multipath capability..
A mobile monitoring and alert sms system with remote configuration – a case s...Editor Jacotech
One of the parent´s main concerns nowadays it to know their children´s whereabouts. Some applications exist to address this issue and most of them rely on internet connection which makes the solution expensive. In this paper we present a low cost solution, based on SMS, and with the ability to remotely configure the child monitoring process. We also present the architecture and the full flowchart of the child application whenever a SMS is received. This case study uses Android and the more recent location API – the Fused Location Provider. For obvious reasons, the security issue has been a concern, which resulted in a configuration module in the child application to specify authorized senders
Leader Election Approach: A Comparison and SurveyEditor Jacotech
In distributed system, the coordinator is needed to manage the use of the resources in the shared environment. Many algorithms have been proposed for the same. They have various positive and negative parts. Here we will discuss those issues which ensure the efficiency of the algorithm for election leader. Here a comparison will be provided to show the advantages and disadvantages of different election algorithms. The comparison would be based on the number of messages passing and the order of time complexity.
Leader election approach a comparison and surveyEditor Jacotech
In distributed system, the coordinator is needed to manage the use of the resources in the shared environment. Many algorithms have been proposed for the same. They have various positive and negative parts. Here we will discuss those issues which ensure the efficiency of the algorithm for election leader. Here a comparison will be provided to show the advantages and disadvantages of different election algorithms. The comparison would be based on the number of messages passing and the order of time complexity
Modeling of solar array and analyze the current transientEditor Jacotech
Spacecraft bus voltage is regulated by power
conditioning unit using switching shunt voltage regulator having
solar array cells as the primary source of power. This source
switches between the bus loads and the shunt switch for fine
control of spacecraft bus voltage. The effect of solar array cell
capacitance [5][6] along with inductance and resistance of the
interface wires between solar cells and power conditioning
unit[1], generates damped sinusoidal currents superimposed on
the short circuit current of solar cell when shunted through
switch. The peak current stress on the shunt switch is to be
considered in the selection of shunt switch in power conditioning
unit. The analysis of current transients of shunt switch in PCU
considering actual spacecraft interface wire length by
illumination of solar panel (combination of series and parallel
solar cells) is difficult with hardware simulation. Software
simulation by modeling solar cell is carried out for a single string
(one parallel) in Pspice [6]. Since in spacecrafts number of
parallels and interface cable length are variable parameters the
analysis of current transients of shunt switch is carried out by
modeling solar array with the help of solar cell model[6] for the
actual spacecraft condition.
Traffic Detection System is an Android application that aims at determining the behavior of traffic in a particular location. It calculates the speed of the vehicle and the level of congestion or the amount of traffic is determined on the basis of the values of sensors. If any such obstruct found, then the driver is provided an option to send messages regarding high traffic to his/her friends. After a distinct number of repeated low speed and breaks, the location of the vehicle (latitude and longitude) send to a pre-specified contact (selected in case of traffic congestion) through an SMS. This application uses the features of the Global positioning system. The Latitude, as well as the longitude of the location where traffic jams are formed, is sent to the friends of the user. The Goggle map of the location also sends to the friends. It uses the SMS Manager a functionality of Android. The friends receiving the messages will thereby avoid taking the congested route and hence the level of traffic on the congested road will decrease, and the friends will reach the destination in comparatively less time.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
TOP 10 B TECH COLLEGES IN JAIPUR 2024.pptxnikitacareer3
Looking for the best engineering colleges in Jaipur for 2024?
Check out our list of the top 10 B.Tech colleges to help you make the right choice for your future career!
1) MNIT
2) MANIPAL UNIV
3) LNMIIT
4) NIMS UNIV
5) JECRC
6) VIVEKANANDA GLOBAL UNIV
7) BIT JAIPUR
8) APEX UNIV
9) AMITY UNIV.
10) JNU
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Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
MOVIE RATING PREDICTION BASED ON TWITTER SENTIMENT ANALYSIS
1. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No 5 Issue No. 1. February 2017
6
MOVIE RATING PREDICTION BASED ON TWITTER
SENTIMENT ANALYSIS
By
1
Mr.Abhishek Kesharwani, 2
Mr. Rakesh Bharti
M.Tech Scholar, UIT, Dr. A. P. J. Abdul Kalam Technical University, Uttar Pradesh, Lucknow, India
Asst Prof., UIT, Dr. A. P. J. Abdul Kalam Technical University, Uttar Pradesh, Lucknow, India
abhishekkesharwani689@gmail.com,goswami.rakesh@gmail.com
ABSTRACT
With microblogging platforms such as Twitter generating
huge amounts of textual data every day, the possibilities of
knowledge discovery through Twitter data becomes
increasingly relevant. Similar to the public voting mechanism
on websites such as the Internet Movie Database (IMDb) that
aggregates movies ratings, Twitter content contains
reflections of public opinion about movies. This study aims to
explore the use of Twitter content as textual data for
predicting the movie rating. In this study, we extract number
of tweets and compiled to predict the rating scores of newly
released movies. Predictions were done with the algorithms,
exploring the tweet polarity. In addition, this study explores
the use of several different kinds of tweet classification
Algorithm and movie rating algorithm. Results show that
movie rating developed by our application is compared to
IMDB and Rotten Tomatoes.
General Terms
Movie rating prediction, tweet polarity
Keywords
Twitter sentiment analysis, comparison of twitter rating from
IMDB and Rotten Tomatoes, tweet classification algorithm,
movie rating algorithm.
1. INTRODUCTION
The task of movie rating prediction by extracting the tweets
and hashtags related to particular movie. Twitter data can be
accessed through the public API provided by the Twitter.
These APIs can be accessed only by authentication requests,
which must be signed with valid login ID and password.
Twitter provides authentication keys for extractions of the
tweets. Tweet extracted from twitter API is collected in
MySql where the tweet is having a unique tweet id, twitter id
and tweet date. Movie name is added by the user and the
release date of the movie is also added by the user. We have
to add the twitter Id of the movie from the twitter and all the
tweets are extracted from the twitter. We have to update the
tweets of the movie by establishing the connection with
twitter from our application, and then all the recent tweets are
added to our database. We have also calculated the popularity
of the movie at the time we extract the tweets. Each time we
want to predict the rating or calculate the popularity of movie
labelling of every tweet is also calculated. Apart from the
tweets obtained from Twitter, the application also calculates
the sentiment associated with the tweet. The application also
stores the sentiment associated with the tweet in the database.
Sentiment of a tweet is categorized as positive, negative,
neutral and irrelevant. In this application we developed a
module that is used to create bag a words from the tweets of
old movies. We create two bags of words positive and
negative .This is the backbone of our application. Each tweet
that we extract from the twitter should be categorised as
positive or negative tweet. We have a module update positive
and negative words in my application which is used for
updating positive and negative bag of words. For calculating
the movie rating we ignored neutral and irrelevant tweets as
these are not useful for any type of information for movie
review. For movie rating prediction we have designed a
module by which you can select any movie and all the tweets
related to that movie is loaded in the algorithm with hashtags
of the movie is also added so that all the review is added for
the particular movie and more precise and accurate rating is
calculated. When we search for a particular movie which is
twitter id of the movie then rating out of 10 is calculated and
change to format of 5 star rating with the help of our
algorithm we design for movie rating. The performance of this
application is evaluated by comparing its results with results
from popular movie rating websites like IMDB and Rotten
Tomatoes. The rating calculated for various movies released
by fetching tweets related to the movie is stored in our
application and real time rating of the movie is calculated and
is represented in tabular form and graphically. The ratings
collected show that the Twitter rating application is following
similar trends as shown in IMDB and Rotten Tomatoes.
Ratings from Twitter application are observed to be of a lower
value. This can be attributed to the fact that the number of
users rating a movie on the other websites is almost 100x
times than what the Twitter application is using. A small
experiment was conducted with varying number of tweets
used by Twitter application to confirm this theory.
2. RELATED WORK
The topic of using social media to predict the future becomes
very popular in recent years. Different work has been already
done using twitter content for predicting the sentiment of
tweets. Movie sentiment analysis based on public tweets [1]
,In this paper they introduced a special approach to
understanding the tongue and frequency of words from one
2. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No 5 Issue No. 1. February 2017
7
sentiment category enabling a much better sentiment
classification compared to the normal machine learning
techniques. They additionally introduce an added sentiment
category - the neutral category. In their analysis they use the
Python programming language with the NLTK library and
compare so obtained results with the normal machine
learning. Predicting Ratings for New Movie Releases from
Twitter Content [5] that is textual knowledge from Twitter
will be seen as an in depth supply of data relating to a
particularly broad form of subjects. With a lot of users
actively expressing themselves on-line, a large quantity of
information is generated daily. Since this knowledge for an
oversized half consists of human expressions, Twitter
knowledge will be seen as a valuable assortment of human
opinion or sentiment, which might be mechanically extracted
with comparatively high accuracy.
3. METHODOLOGY
The overall system can be designed in following phases
i. Tweet Collection
ii. Tweet Classification
iii. Rating Movies.
3.1 Tweet Collection
Twitter data can be accessed through the public API provided
by the Twitter. These APIs can be accessed only by
authentication requests, which must be signed with valid login
ID and password. Twitter provides authentication keys for
extractions of the tweets. We have to follow some steps to
create Authentication keys.
i. Create application on twitter.
ii. Manage Application
iii. Change the permissions to read and write.
iv. Retrieve Authentication keys.
First we create application on twitter by signing in to
https://apps.twitter.com/app/new. Second Step we have to
manage Application. In the third step change the application
permissions to read and write. After the completion of the
Application ,we have the following unique keys that is
required to fetch tweets from twitter
a. Consumer key
b. Consumer Secret key
c. Access token
d. Access token secret
Tweet Extracted from twitter having complete information
like date of tweet, tweet ID, user ID, retweet count etc.We
will use only tweet date, tweet ID and tweet. We will add two
more columns release date and added by to have some extra
information about the analysis of movie. We have integrated
twitter API into our application so as to fetch all the tweets
related to a particular movie and all the news and comments
related to a particular movie. There are so many limitation
related to this API as this will extract limited tweets
approximately hundred tweets in one time. But the tweets are
collected into the database as we have MySql database in our
back end. We have to update the tweets of the movie by
establishing the connection with twitter from our application,
and then all the recent tweets are added to our database.
3.2 Tweet Classification
In this module I label every tweet as positive, negative,
neutral and irrelevant. Each time we want to predict the rating
or calculate the popularity of movie and labeling of every
tweet is also calculated. Apart from the tweets obtained from
Twitter, the application also calculates the sentiment
associated with the tweet. In this we tokenize the tweet into
different tokens separated by space and compare each token
with our predefined set of positive and negative bag of words.
After the comparison of tokens we find the total number of
positive and negative tokens in the tweet. Count the total
number of positive and negative tokens in the tweet and label
them as p and n respectively. Calculate the value of ratio as
total number of positive tokens to the total number of positive
and negative token.
Table 1 Table for tweet labeling
Ratio Tweet Label
ratio>0.5 Positive
ratio=0.5 Neutral
ratio<0.5 Negative
ratio=0/0 (p==0 && n==0) Irrelevant
3.2.1 Bag of words
In this application we developed a module that is used to
create bag a words from the tweets of old movies. We create
two bags of words positive and negative .These are the
backbone of our application. Each tweet that we extract from
the twitter should be categorized as positive or negative tweet.
We have a module update positive and negative words in my
application. In this module if we want to add any positive or
negative word ,if that word already exist in the database of
bag of words then it is not updated.
3.3 Algorithm for Tweet Classification
1. Extract the tweet from twitter using Twitter API.
2. Store the tweet in the database. Each tweet has a unique id
and date of tweet which is also store in the database.
3. For each tweet we have to specify the tweet label. Tweet
labels are positive, negative, neutral and irrelevant.
4. Calculate the value of p and n i.e. total number of positive
and negative words obtained by comparing each word
stored in our manually collected positive and negative
collection of bag of word.
5. Classify the tweet label of each tweet using the following
formula
ratio= p/ (p + n)
If (ratio>0.5)
{
Tweet label="positive";
}
If (ratio==0.5)
{
Tweet label="neutral";
}
If (ratio<0.5)
{
3. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No 5 Issue No. 1. February 2017
8
Tweet label="negative";
}
If (p==0 and n==0)
{
Tweet label="irrelevant";
}
6. Here p and n are total number of positive and negative
words obtained by comparing each word stored in our
manually collected positive and negative collection of
word set.
3.4 Rating Movie
Rating is calculated by the total number of positive tweets
over total tweets. This was designed that the rating is as
correct because it will get the most recent views about the
particular movie and twitter chat about the popularity and
criticism of the movie
3.5 Algorithm for Rating Movie
1. Update the tweets and hashtag of a movie with twitter id
and store it into database.
2. Select all the tweets and hashtags from the database with
same twitter id and apply tweet classification algorithm.
3. Classify all the tweets and store the tweets with tweet
labels as positive, negative, neutral and irrelevant.
4. Ignore neutral and irrelevant tweets. Neutral tweet does
not specify any positive or negative sentiment as they
create a situation of ambiguity and irrelevant tweets do
not specify any sentiment.
5. Calculate the value of rating as
rating = ((total positive tweets) / (total positive tweets
+total negative tweets))*10)
6. if (rating<2.0 and rating >=0)
{
Movie rating = 1 star
}
if ( rating>=2 and rating<3 )
{
Movie rating = 2 star
}
if (rating>=3 and rating<4 )
{
Movie rating = 2.5 star
}
if (rating>=4 and rating<5 )
{
Movie rating = 3 star
}
If (rating>=5 and rating<6)
{
Movie rating = 3.5 star
}
If (rating>=6 and rating<8 )
{
Movie rating = 4 star
}
If (rating>=8 and rating<9)
{
Movie rating = 4.5 star
}
If (rating>=9 and rating <=10)
{
Movie rating = 5 star
}
4. EXPERIMENTAL RESULTS AND
PERFORMANCE ANALYSIS
The performance of this application is evaluated by
comparing its results with results from popular movie rating
websites like IMDB and Rotten Tomatoes. IMDB is internet
information of data associated with films, television,
programs etc. the location permits registered users to rate any
film on a scale of one to ten, except for writing reviews
regarding it. The location displays a weighted mean of user
ratings and displays it next to the movie title. This website has
6.05 Daily Page views per visitor [7] and a median of fifteen
million individuals visit the web site per month. Rotten
Tomatoes may be a web site dedicated to film reviews and
news. It offers 2 sorts of scores for movies – Tomato meter
critic mixture score and Audience score. The Critic mixture
score reflects reviews and ratings from varied newspaper
writers or from those that belong to film critic associations.
The Audience score is calculated supported user’s reviews
and ratings. For the aim of confirmatory this application,
solely Audience score is taken into account. Registration is
free however the location requests permission to look at user’s
social network profiles. It gets 3.60 Daily Page views per
visitor and a median of thirteen million individuals visit the
web site per month. [8].The rating calculated for various
movies released by fetching tweets related to the movie is
stored in our application and real time rating of the movie is
calculated and is represented in tabular form and graphically
as shown in table 2 and figure 1.
Table 2 Twitter Rating vs. IMDB and Rotten tomatoes
Movie
Name
Twitter
Rating IMDB
Rotten
Tomatoes
Ae dil hai
mushkil 2 3 2.9
Shivaay 3.5 3.5 3.8
Force2 3 3 3.2
Pink 3 4 4.2
Raees 2 4 3.9
4. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No 5 Issue No. 1. February 2017
9
Kaabil 4 4 4
Dangal 3.5 4.5 4.6
Kahaani2 4 3.5 3.7
Bahubali 2.5 4 4.3
As shown in the Figure 1 twitter rating for the movie “Ae Dil
hai Mushkil” is 2 while that IMDB is 3 and rotten tomatoes
rated it 2.9.For the movie “Kaabil” we predict the same rating
as predicted by the IMDB and Rotten Tomatoes. For the
movie “kahaani2” we predict rating as 4 but IMDB rated as
3.5 and rotten tomatoes rated 3.7.
Fig 1: Twitter Rating versus IMDB and Rotten Tomatoes
Table 3 Twitter Reviews versus IMDB and Rotten
Tomatoes Reviews
Movie
Name
Twitter
Reviews
IMDB
Reviews
Rotten
Tomatoes
Reviews
Ae dil hai
mushkil 1375 12644 921
Shivaay 392 6341 154
Force2 233 2245 50
Pink 199 18102 302
Raees 431 14390 332
Kaabil 1200 7462 131
Dangal 193 30720 1433
Kahaani2 200 1719 204
Bahubali 431 67698 1743
As show in Table 3 of Twitter Reviews and IMDB and Rotten
Tomatoes reviews, the number of reviews are very large for
IMDB as compared to twitter reviews for the movie “Ae dil
5. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No 5 Issue No. 1. February 2017
10
hai mushkil” total number of tweets we collected are 1375
while that of IMDB is 12644 and that of Rotten Tomatoes we
obtained the ratings as 2 for twitter, 3 for IMDB and 2.9 for
Rotten Tomatoes. For the movie “Kaabil” total number of
tweets we collected are 1200 while that of IMDB is 7462 and
that of Rotten Tomatoes 131 we obtained the ratings as 4 for
all are same. The result shows that if we obtain huge amount
of data from twitter than we calculate more accurate rating by
our algorithm as this application works more accurately with
huge amount of data, but currently the application can retrieve
close to 100 tweets per API call, depending on how recently
the movie was tweeted about. It has to be stressed that this is
not enough data to determine the opinion of the Crowd for
rating a movie. This is especially the case when dealing with
Twitter data retrieval based on keyword search. The search
results invariably include advertisements, retweets, and spam.
These have to be filtered out or categorized separately in order
to not interfere with the sentiment classification. Since Twitter
has a rate limit on data available for use, the workaround for
this issue in the application is to store the tweets every time it
is retrieved, and calculate a new rating when the movie name
is searched again. These results in an increase of data used to
review movies, every time the application is used.
5. CONCLUSION
Twitter data effectively manages to capture the opinions and
emotions of the crowd and Twitter APIs make it fairly easy to
gather this information and analyze it. This desktop
application indeed manages to use this massive amount of
data to provide a meaningful and useful result due to the
speed limit introduced by Twitter, this is often presently an
educational implementation of the thought to use twitter
knowledge for rating a movie, In future if the limit on the
information of twitter is removed then this application has
excellent result and may be used for thus several merchandise
review and its quality. If all the tweets containing the search
string for a movie name will be captured and analyzed, a lot
of precise conclusion will be drawn.
The training data used by the classification algorithm are very
limited for tweets per category. The results have been
encouraging with the small set; I expect the result to be even
more impressive with a larger selection of natural language
processing in the training data. For some movies we predicted
the exact rating but in some cases we are not getting exact
result as we have some less data for that movie.
As the application is used frequently, the dataset grows with
it. This results in the rating of a movie always being up to date
with public opinion. The best use case is to search for recent
movies, simply because that is when the crowd seems to be
tweeting most about the movies then more accurate rating will
be predicted.
6. FUTURE WORK
In future enhancements, more categories can be introduced to
classify tweets – extremely positive, mildly positive,
extremely negative, mildly negative, neutral, and irrelevant.
This can be used to improve the rating formula to make it
more accurate. A weight can be associated with each category
and then calculate the average.
More number of classes implies increase in number of
attributes for the classifier to compare the input with. This
should show some difference in the outcome of the classifier
and hence difference in the rating shown by the application.
We only consider the data from twitter but in future data from
other social media like facebook, YouTube and other blogger
comments is also taken into considerations for more accurate
rating and with real opinions from all the social media into
single place.
7. REFERENCES
[1] Aljaˇz Blatnik, Kaja Jarm, Marko Meˇza, Movie sentiment
analysis based on public tweets, Faculty of Electrical
Engineering, University of Ljubljana, Trˇzaˇska 25, 1000
Ljubljana, Slovenia, 81(4): 160–166, 2014 ORIGINAL
SCIENTIFIC PAPER.
[2] Snehal. A. Mulay, Shrijeet J Joshi, Mohit R Shaha,
Hrishikesh V Vibhute, Mahesh P Panaskar, Sentiment
Analysis and Opinion Mining With Social Networking for
Predicting Box Office Collection of Movie, International
Journal of Emerging Research in Management &Technology
ISSN: 2278-9359 (Volume-5, Issue-1)
[3] Ladislav Peska, Peter Vojtas, Hybrid Biased k-NN to
Predict Movie Tweets Popularity, Faculty of Mathematics and
Physics Charles University in Prague Malostranske namesti
25, Prague, Czech Republic.
[4] Vasu Jain, Prediction of Movie Success using Sentiment
Analysis of Tweets Department of Computer Science,
University of Southern California,
The International Journal of Soft Computing and Software
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[5] Wernard Schmit, Sander Wubben, Predicting Ratings for
New Movie Releases from Twitter Content, Proceedings of
the 6th Workshop on Computational Approaches to
Subjectivity, Sentiment and Social Media Analysis (WASSA
2015), pages 122–126,Lisboa, Portugal, 17 September, 2015.
c 2015 Association for Computational Linguistics.
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[7] Alexa. Statistics for IMDB, available at
http://www.alexa.com/siteinfo/imdb.com
[8] Alexa. Statistics for Rotten Tomatoes, available at
http://www.alexa.com/siteinfo/rottentomatoes.com
[9] Twitter, Inc. Twitter Help Center - Using hashtags on
Twitter. https://support.twitter.com/entries/49309 (accessed
February 2017)
[10] Twitter, Inc. Discover Twitter - What is Twitter and how
to use it. https://discover.twitter.com (accessed February
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(accessed December, 2016).
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