In a study, it was investigated relationship among stock market movement and Tweeter feed
content. We are expecting to see if there is connection among sentiment information extracted from the Tweets
using a Vader in predicting movements of stock prices. As a result it was obtained strong positive correlation with
a coefficient of correlation to be 0.7815.
I created this presentation for a client who wanted to understand how blockchain technology can be used in healthcare, particularly for eHR (electronic health record). They wanted a non-technical overview.
U.S. Road Accidents Data Analysis and VisualizationMrinalini Sundar
With the US accident rate as a case study, we are showing you how automated, code-free integration for data housed in major platforms to Azure/Snowflake/Amazon Redshift or Google BigQuery is super easy with Datom.ai. All data transfers are done with a drag and drop interface and are based on a transparent pricing mechanism based on actual usage.
Implementing Blockchain applications in healthcarePistoia Alliance
Blockchain technology can revolutionise the way information is exchanged between parties by bringing an unprecedented level of security and trust to these transactions. The technology is finding its way into multiple use cases but we are yet to see full adoption and real-world business implementation in the Healthcare industry.
In this webinar we will explore the main challenges and considerations for the implementation of Blockchain technology in Healthcare use cases. This is the third webinar in our Blockchain Education series.
This presentation consist of detail description regarding how social media sentiments analysis is performed , what is its scope and benefits in real life scenario.
Presentation based on recent Book: Natural Language Processing for Social Media (A. Farzindar and D. Inkpen, Morgan & Claypool Publishers, 2015, 2nd Edition Dec. 2017)
I created this presentation for a client who wanted to understand how blockchain technology can be used in healthcare, particularly for eHR (electronic health record). They wanted a non-technical overview.
U.S. Road Accidents Data Analysis and VisualizationMrinalini Sundar
With the US accident rate as a case study, we are showing you how automated, code-free integration for data housed in major platforms to Azure/Snowflake/Amazon Redshift or Google BigQuery is super easy with Datom.ai. All data transfers are done with a drag and drop interface and are based on a transparent pricing mechanism based on actual usage.
Implementing Blockchain applications in healthcarePistoia Alliance
Blockchain technology can revolutionise the way information is exchanged between parties by bringing an unprecedented level of security and trust to these transactions. The technology is finding its way into multiple use cases but we are yet to see full adoption and real-world business implementation in the Healthcare industry.
In this webinar we will explore the main challenges and considerations for the implementation of Blockchain technology in Healthcare use cases. This is the third webinar in our Blockchain Education series.
This presentation consist of detail description regarding how social media sentiments analysis is performed , what is its scope and benefits in real life scenario.
Presentation based on recent Book: Natural Language Processing for Social Media (A. Farzindar and D. Inkpen, Morgan & Claypool Publishers, 2015, 2nd Edition Dec. 2017)
Loan Prediction system is a system which provides you a interface for loan approval to the applicants application of loan. Applicants provides the system about their personal information and according to their information system gives his status of availability of loan.
Stock market prediction using Twitter sentiment analysisjournal ijrtem
ABSTRACT : In a study, it was investigated relationship among stock market movement and Tweeter feed content. We are expecting to see if there is connection among sentiment information extracted from the Tweets using a Vader in predicting movements of stock prices. As a result it was obtained strong positive correlation with a coefficient of correlation to be 0.7815.
KEYWORDS : correlation, financial market, polarity, sentiment analysis, tweets
R vs SPSS: Which One is The Best Statistical LanguageStat Analytica
Want to differentiate between R vs SPSS. Here is the best ever comparison between R vs SPSS. Watch each slide of this presentation to clear your doubts between R vs SPSS.
Everything Blockchain is a development, engineering, and services company specializing in Blockchain technologies and decentralized processing. The Company works with clients to develop custom Blockchain payment solutions settled in fiat and cryptocurrencies. Everything Blockchain also designs proprietary security measures and protocols for Blockchain technologies with overlay and licensing revenue opportunities. Utilizing tools within the Blockchain ecosystem to enhance overall performance, the Company continually evaluates new growth opportunities in mining, acquiring, and utilizing cryptocurrencies. Involved in the early-stage launch of HEX, the internet’s first cryptographic certificate of deposit, the Company has earned $5 million of tokens to date and expects to be awarded another $5 million of tokens in the coming months. Staking operations, which can earn interest of 5%-40%, provide Everything Blockchain with additional opportunities for growth and predictable revenue streams. With a highly accomplished management and board, expert team, and access to industry leaders and disruptors, Everything Blockchain is at the forefront of innovative and lead generating technologies.
Sentiment analysis using naive bayes classifier Dev Sahu
This ppt contains a small description of naive bayes classifier algorithm. It is a machine learning approach for detection of sentiment and text classification.
A MODEL BASED ON SENTIMENTS ANALYSIS FOR STOCK EXCHANGE PREDICTION - CASE STU...csandit
Predicting the behavior of shares in the stock market is a complex problem, that involves variables not always known and can undergo various influences, from the collective emotion to high-profile news. Such volatility, can represent considerable financial losses for investors. In order to anticipate such changes in the market, it has been proposed various mechanisms to try to predict the behavior of an asset in the stock market, based on previously existing information.
Such mechanisms include statistical data only, without considering the collective feeling. This article, is going to use natural language processing algorithms (LPN) to determine the collective mood on assets and later with the help of the SVM algorithm to extract patterns in an attempt to predict the active behavior. Nevertheless it is important to note that such approach is not intended to be the main factor in the decision making process, but rather an aid tool, which combined with other information, can provide higher accuracy for the solution of this problem
A MODEL BASED ON SENTIMENTS ANALYSIS FOR STOCK EXCHANGE PREDICTION - CASE STU...cscpconf
Predicting the behavior of shares in the stock market is a complex problem, that involves variables not always known and can undergo various influences, from the collective emotion to the high-profile news. Such volatility can represent considerable financial losses for investors. In order to anticipate such changes in the market, it has been proposed various mechanisms to try to predict the behavior of an asset in the stock market, based on previously existing information. Such mechanisms include statistical data only, without considering the collective feeling. This article is going to use natural language processing algorithms (LPN) to determine the collective mood on assets and later with the help of the SVM algorithm to extract patterns in an
attempt to predict the active behavior. Nevertheless it is important to note that such approach is not intended to be the main factor in the decision making process, but rather an aid tool, which combined with other information, can provide higher accuracy for the solution of this problem.
Loan Prediction system is a system which provides you a interface for loan approval to the applicants application of loan. Applicants provides the system about their personal information and according to their information system gives his status of availability of loan.
Stock market prediction using Twitter sentiment analysisjournal ijrtem
ABSTRACT : In a study, it was investigated relationship among stock market movement and Tweeter feed content. We are expecting to see if there is connection among sentiment information extracted from the Tweets using a Vader in predicting movements of stock prices. As a result it was obtained strong positive correlation with a coefficient of correlation to be 0.7815.
KEYWORDS : correlation, financial market, polarity, sentiment analysis, tweets
R vs SPSS: Which One is The Best Statistical LanguageStat Analytica
Want to differentiate between R vs SPSS. Here is the best ever comparison between R vs SPSS. Watch each slide of this presentation to clear your doubts between R vs SPSS.
Everything Blockchain is a development, engineering, and services company specializing in Blockchain technologies and decentralized processing. The Company works with clients to develop custom Blockchain payment solutions settled in fiat and cryptocurrencies. Everything Blockchain also designs proprietary security measures and protocols for Blockchain technologies with overlay and licensing revenue opportunities. Utilizing tools within the Blockchain ecosystem to enhance overall performance, the Company continually evaluates new growth opportunities in mining, acquiring, and utilizing cryptocurrencies. Involved in the early-stage launch of HEX, the internet’s first cryptographic certificate of deposit, the Company has earned $5 million of tokens to date and expects to be awarded another $5 million of tokens in the coming months. Staking operations, which can earn interest of 5%-40%, provide Everything Blockchain with additional opportunities for growth and predictable revenue streams. With a highly accomplished management and board, expert team, and access to industry leaders and disruptors, Everything Blockchain is at the forefront of innovative and lead generating technologies.
Sentiment analysis using naive bayes classifier Dev Sahu
This ppt contains a small description of naive bayes classifier algorithm. It is a machine learning approach for detection of sentiment and text classification.
A MODEL BASED ON SENTIMENTS ANALYSIS FOR STOCK EXCHANGE PREDICTION - CASE STU...csandit
Predicting the behavior of shares in the stock market is a complex problem, that involves variables not always known and can undergo various influences, from the collective emotion to high-profile news. Such volatility, can represent considerable financial losses for investors. In order to anticipate such changes in the market, it has been proposed various mechanisms to try to predict the behavior of an asset in the stock market, based on previously existing information.
Such mechanisms include statistical data only, without considering the collective feeling. This article, is going to use natural language processing algorithms (LPN) to determine the collective mood on assets and later with the help of the SVM algorithm to extract patterns in an attempt to predict the active behavior. Nevertheless it is important to note that such approach is not intended to be the main factor in the decision making process, but rather an aid tool, which combined with other information, can provide higher accuracy for the solution of this problem
A MODEL BASED ON SENTIMENTS ANALYSIS FOR STOCK EXCHANGE PREDICTION - CASE STU...cscpconf
Predicting the behavior of shares in the stock market is a complex problem, that involves variables not always known and can undergo various influences, from the collective emotion to the high-profile news. Such volatility can represent considerable financial losses for investors. In order to anticipate such changes in the market, it has been proposed various mechanisms to try to predict the behavior of an asset in the stock market, based on previously existing information. Such mechanisms include statistical data only, without considering the collective feeling. This article is going to use natural language processing algorithms (LPN) to determine the collective mood on assets and later with the help of the SVM algorithm to extract patterns in an
attempt to predict the active behavior. Nevertheless it is important to note that such approach is not intended to be the main factor in the decision making process, but rather an aid tool, which combined with other information, can provide higher accuracy for the solution of this problem.
The economic growth is a consensus in any country. To grow economically, it is necessary to channel the
revenues for investment. One way of raising is the capital market and the stock exchanges. In this context,
predicting the behavior of shares in the stock exchange is not a simple task, as itinvolves
variables not always known and can undergo various influences, from the collective emotion to
high-profile news. Such volatility can represent considerable financial losses for investors. In
order to anticipate such changes in the market, it has been proposed various mechanisms trying
to predict the behavior of an asset in the stock market, based on previously existing information.
Such mechanisms include statistical data only, without considering the collective feeling. This
paper is going to use natural language processing algorithms (LPN) to determine the
collective mood on assets and later with the help of the SVM algorithm to extract patterns in an
attempt to predict the active behaviour.
Measuring human and Vader performance on sentiment analysisjournal ijrtem
ABSTRACT: Sentiment analysis was examined on Tweeter data and neutral polarity was excluded out of research. In this paper on Tweets were determined polarities in two ways: by group of ten people and also by Vader sentiment analysis. In total was examined 527 Tweets from 10 different companies. At the end obtained results were compared to see if there is significant similarity among the methodologies. Results showed that there is no significant difference among human and Vader sentiment.
KEYWORDS: human, polarity, sentiment analysis, Vader
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.
An Assessment of Sentiment Analysis of Covid 19 Tweetsijtsrd
Various rumors and assumptions have circulated about the COVID 19 immunization, making it a heated subject of discussion in India. This prompted a reaction from the countrys populace, who During the course of favorable, negative, and neutral evaluations, tweets and retweets on twitter. The number of these tweets are a jumble of unstructured data. The goal of this study is to have the statistics justify feeling implied by it. The purpose of this study is to take advantage of twitters massive data pool and extract insights that have the implications that can be drawn from it. Comprehensive research on the peoples feelings may help us arrive at a fair familiarity with the population at larges point of view toward preventing disease by vaccination. Dataset taken into consideration for vaccination related tweets are collected for study. From 2020 to 2021, including a data mining of 16,05,152 tweets related to vaccination. Ms. Tanzeela Qureshi | Dr. Mohit Singh Tomar | Dr. Ritu Shrivastava "An Assessment of Sentiment Analysis of Covid-19 Tweets" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-5 , October 2023, URL: https://www.ijtsrd.com/papers/ijtsrd59976.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/59976/an-assessment-of-sentiment-analysis-of-covid19-tweets/ms-tanzeela-qureshi
In the age of social media communication, it is easy to
modulate the minds of users and also instigate violent
actions being taken by them in some cases. There is a need
to have a system that can analyze the threat level of tweets
from influential users and rank their Twitter handles so
that dangerous tweets can be avoided going public on
Twitter before fact-checking which can hurt the sentiments
of people and can take the shape of violence. The study
aims to analyse and rank twitter users according to their
influential power and extremism of their tweets to help
prevent major protests and violent events. We scraped top
trending topics and fetched tweets using those hashtags.
We propose a custom ranking algorithm which considers
source based and content based features along with a
knowledge graph which generates the score and rank the
twitter users according to the scores. Our aim with this
study is to identify and rank extremist twitter users with
regards to their impact and influence. We use a technique
that takes into consideration both source based and
content-based features of tweets to generate the ranking of
the extremist twitter users having a high impact factor
CATEGORIZING 2019-N-COV TWITTER HASHTAG DATA BY CLUSTERINGijaia
Unsupervised machine learning techniques such as clustering are widely gaining use with the recent increase in social communication platforms like Twitter and Facebook. Clustering enables the finding of patterns in these unstructured datasets. We collected tweets matching hashtags linked to COVID-19 from a Kaggle dataset. We compared the performance of nine clustering algorithms using this dataset. We evaluated the generalizability of these algorithms using a supervised learning model. Finally, using a selected unsupervised learning algorithm we categorized the clusters. The top five categories are Safety,
Crime, Products, Countries and Health. This can prove helpful for bodies using large amount of Twitter data needing to quickly find key points in the data before going into further classification.
Categorizing 2019-n-CoV Twitter Hashtag Data by Clusteringgerogepatton
Unsupervised machine learning techniques such as clustering are widely gaining use with the recent increase in social communication platforms like Twitter and Facebook. Clustering enables the finding of patterns in these unstructured datasets. We collected tweets matching hashtags linked to COVID-19 from a Kaggle dataset. We compared the performance of nine clustering algorithms using this dataset. We evaluated the generalizability of these algorithms using a supervised learning model. Finally, using a selected unsupervised learning algorithm we categorized the clusters. The top five categories are Safety, Crime, Products, Countries and Health. This can prove helpful for bodies using large amount of Twitter data needing to quickly find key points in the data before going into further classification.
CATEGORIZING 2019-N-COV TWITTER HASHTAG DATA BY CLUSTERINGgerogepatton
Unsupervised machine learning techniques such as clustering are widely gaining use with the recent increase in social communication platforms like Twitter and Facebook. Clustering enables the finding of patterns in these unstructured datasets. We collected tweets matching hashtags linked to COVID-19 from a Kaggle dataset. We compared the performance of nine clustering algorithms using this dataset. We evaluated the generalizability of these algorithms using a supervised learning model. Finally, using a selected unsupervised learning algorithm we categorized the clusters. The top five categories are Safety, Crime, Products, Countries and Health. This can prove helpful for bodies using large amount of Twitter data needing to quickly find key points in the data before going into further classification.
Using Social Media to Measure the Consumer Confidence: The Twitter Case in SpainManu García
The goal of this project is to make an index which contains the consumer confidence. The source of information used to create this indicator has been Twitter, the methodology comes from opinion mining sentiment analysis and the metric used to check if this information can be usefull is the pearson's correlation coeficient.
There is evidence that the consumer confidence can be found in social media and these findings might be usefull to create alternative indexes in shorter intervals of time, or even in regional basis.
Twitter Based Sentimental Analysis of Impact of COVID-19 on Economy using Naï...CSCJournals
COVID-19 outbreak brought unprecedented changes to people’s lives and made significant impact on the US and world economy. It wrought havoc on livelihood, businesses and ultimately the economy. Understanding how the sentiment on economy is changing and main factors that drives the change will help the public to make sense of the impact and generating relief measures. In this paper we present a novel Naïve Bayes model using a word-based training approach to perform the analysis and determine the sentiment of Twitter posts. The novelty of this methodology is that we use labelled set of words to classify the tweets to perform sentimental analysis as opposed to the more expensive methods of manually classifying the tweets. We then perform analysis on the resulting labelled tweets to observe the trend of economy from February 2020 to July 2020 and determine how COVID-19 impacted the economy based on what people posted on Twitter. We found our data was largely inclined towards negative sentiment indicating that the economy had been largely negatively impacted as a result of COVID-19. Further, we correlate the sentiment with the stock market index aka Dow Jones Industrial Average (DJIA) because stock market movement closely mirrors the economic sentiment and is shown as one of the main factors influencing people's attitude change from our sentimental analysis. We found strong correlation between the two, indicating stock market change is one of the driving factors behind people's opinion change about economy during pandemic. This work proposed and tested a generic lower-cost text-based model to analysis generic public’s opinion about an event which can be adopted to analyze other topics.
IEEE PROJECTS 2016 - 2017
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It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Project Domain list 2016
1. IEEE based on datamining and knowledge engineering,
2. IEEE based on mobile computing,
3. IEEE based on networking,
4. IEEE based on Image processing,
5. IEEE based on Multimedia,
6. IEEE based on Network security,
7. IEEE based on parallel and distributed systems
Project Domain list 2016
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2016
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
5. IOT Projects
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Final Year students of
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2. BCA/B.E(C.S)
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6. MSc (IT)
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9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
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4. VB
5. SQL SERVER
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Tweetfix is a visualization platform, developed for the Fix the Fixing european project, where users can explore the results of crowdsourced data analytics from Social Media on well-known Match Fixing cases.
FRAMEWORK FOR ANALYZING TWITTER TO DETECT COMMUNITY SUSPICIOUS CRIME ACTIVITYcscpconf
This research work discusses how an integrated open source intelligence framework can help the law enforcements and government entities who are investigating crimes based on statistical and graph analysis on Twitter data. The solution supports a real-time and off-line analysis of the tweets collections. The framework employs tools that support big data processing capabilities, to collect, process and analyze a huge amount of data. The outline solution supports content and textual based analysis, helping the investigators to dig into a person and the community linked to that person based on a tweet. Our solution supports an investigative processes composed of the following phases (i) find suspicious tweets and individuals based on hash tags analysis (ii) classify the user profile based on Twitter features (iii) identify influencers in the FOAF networks of the senders (iiii) analyze these influencers’ background and history to find hints of past or current criminal activity.
Similar to Stock market prediction using Twitter sentiment analysis (20)
The effect of functionalized carbon nanotubes on thermalmechanical performanc...IJRTEMJOURNAL
The new approaches for preparing nanocomposite coating by modificated carbon nanonotubes
(CNTs) and epoxy resin was done in the study. thermal-mechanical performance of nanocomposite coating was
investigated and the results were reported in this paper. The physic-chemical techniques such as Differential
scanning calorimetry (DSC) and Thermal gravimetric analysis (TGA) were used to characterize the thermal
performance of Epoxy nanocomposite coating. The test techniques for mechanical properties of paint coating as
adhesion, hardness, impact resistance and bending strength were employed in the work. The results indicated
that CNTs were dispersed in epoxy coating with only ratio of 0.1 wt% enhanced the Glass Transition
Temperature (Tg), decomposition temperature of epoxy coating and improved mechanical properties
significantly. Also functionalized CNTs can be reinforced thermal-mechanical of the epoxy coating better than
neat CNTs.
Study of desalination processes of seawater from the desalination plant of La...IJRTEMJOURNAL
The use of water for food purposes requires excellent physicochemical quality. To contribute to
the control of water quality. Water treated by reverse osmosis is aggressive and demineralize can not be used
directly as a source of drinking water. The objective of this work is to study, physics-chemical analyzes of raw
water, pretreated osmosis and treated (permeate) and produced water (reservoir) at the desalination plant of
seawater Laayoune (SDL), located in southern Morocco. For this, we have followed several qualitative
parameters such as pH, conductivity, turbidity
Multi products storage using randomnessIJRTEMJOURNAL
The following Project shows the benefits of a research established into a multi-products
warehouse belongs to an automotive industry supplier. The main goal was applied a tool recognizing the rules
for distribution and material storage. Once the research was completed, the benefits were, the idle times
reduction per hours/week by the two initial processes. The politics for storage assignment and location, propose
a system to improve the space into this areain order to avoid material management and flow issues. It is
important to mention, the system proposed could be applied into warehouses with storage size and space
restricted by sorting area, also different material types, production settings and physical specifications for
which set warehouses with traditional management of distribution without slack, involves lack of materials,
pieces without records, incorrect location assigned, stock error.
Existence results for fractional q-differential equations with integral and m...IJRTEMJOURNAL
This paper concerns a new kind of fractional q-differential equation of arbitrary order by
combining a multi-point boundary condition with an integral boundary condition. By solving the equation which
is equivalent to the problem we are going to investigate, the Green’s functions are obtained. By defining a
continuous operator on a Banach space and taking advantage of the cone theory and some fixed-point theorems,
the existence of multiple positive solutions for the BVPs is proved based on some properties of Green’s functions
and under the circumstance that the continuous functions f satisfy certain hypothesis. Finally, examples are
provided to illustrate the results.
A study on financial aspect of supply chain managementIJRTEMJOURNAL
The more common approaches used in the SCM consider only the physical logistic operations
and ignore the financial aspects of the supply chain. The main objective to incorporate financial aspects in
supply chain management is to strengthen managerial decisions concerning financial flows in supply chains,
while empirical knowledge about financial supply chain management (FSCM) is in its early stages. This paper
presents a model for FSCM which financial planning in addition to operation planning is decided in it. The
main contribution of this paper is to define two approaches for Financial Supply Chain Management and to
compare them. This financial approaches are: Traditional financial approach and new financial approach.
Traditional financial approach integrates physical goods flows and financial flows. New financial approach
considers in making decisions other financial indicators such as market to book value, liquidity ratios, capital
structure ratios, and return on equity, sales margin, turnover ratios and stock security ratios, among others.
Moreover, the new approach applies the change in equity instead of the traditional approach measures of profit
as the objective function to be maximized in the presented model. To show the attributes of the presented
approaches, the results of the new approach and the traditional approach is compared. The findings indicate
that the traditional approach leads to lower change in equity compared to the financial approach. Also, the
results clearly reveal the better improvement of using the new approach over the traditional approach, and
convince the decision makers to take advantage of the new approach.
Rural Livelihood and Food Security: Insights from Srilanka Tapu of Sunsari Di...IJRTEMJOURNAL
Food security is the foremost need of every human society. It is a fundamental right and
government responsibility but still food insecurity is prevalent in rural areas of least developed nations. To cope
with food insecurity, undertaking diverse income generating activities is common as well as key strategy adopted
by rural people. The objective of this study is to assess rural livelihood and food security status of a remote island
named Srilanka Tapu of Sunsari district. A random sampling technique was used to collect primary data from 40
rural household heads using semi-structured questionnaire. Descriptive methods were used for analyzing. The
findings revealed that the food security situation of the Tapu is insecure. Most basic infrastructures and social
services needed for people livelihood such as road, electricity sufficient food availability, education, healthcare,
sanitation, etc. were found to be extremely poor. Most of the households are small scale farmers involving
themselves in diverse livelihood activities which are mostly temporary, low-skilled and low paying. However,
people are fulfilling their food needs at every cost but are highly vulnerable to food insecurity. Also, their lives
security is equally vulnerable because of disastrous Koshi River flooding which occurs every year in the Tapu.
The findings therefore critically suggest that food security of remote and vulnerable human settlements should be
at top priority in policy formulation and implementation level. The study also recommends a need for an in-depth
research for making evidence based policy interventions for improvement of diversify rural livelihood along with
sustainable environment
With mounting concerns over the state of our planet, there is continuing demand that chemists
and chemical engineers should develop greener chemical processes and products. In the 1990s, with the
growing awareness of the hazardous impacts of the chemical industry, the green chemistry revolution was
launched by American chemists Paul T. Anastas and John Warner. Green chemistry is the kind of chemistry that
seeks to minimize pollution, conserve energy, and promote environmentally friendly production. This paper
provides a brief introduction to green chemistry.
Assessment of Building Failure: The Case of Saint Thomas’s Anglican Church, A...IJRTEMJOURNAL
There have been incessant reports of the collapse of buildings resulting in the loss of lives and
properties globally. However, there has been a dearth of information regarding any findings about the collapse
of building structures. An extensive study of causes of selected building collapse in Nigeria and abroad is carried
out in this work by visiting some locations of building collapse, reading journals and newspaper articles on
structural defects and testing rubbles collected from collapsed areas. This study therefore examined the general
causes of the collapse of some buildings particularly the reasons for the collapse of Saint Thomas’s 2-storey
Church Hall, Akure. Laboratory testing was carried out in this study to investigate the causes of collapse using
samples from the site of the collapsed building. An appraisal of the structural drawings of the collapsed building
was also investigated. Findings revealed that the building collapsed due to poor design, bad construction
materials and inadequate supervision. The paper concludes that buildings collapse can be reduced in Nigeria by
avoiding all. It recommended use of only duly registered professionals in the building industry for construction.
Data warehousing is a technique for collecting and managing data from multiple internal and
external sources to provide meaningful business insights. Data warehouses are designed to give a long-range
view of data over time and provide a decision support system environment. They are a vital component of
business intelligence, which is designed for data analysis and reporting. They are used to provide greater
insight into the performance of a business. This paper provides a brief introduction on data warehousing
Resource recycling and waste-to-energy: The cornerstones of circular economyIJRTEMJOURNAL
"Circular Economy" is the pursued goal of sustainable development of mankind for the 21st
century. In short, the fundamental spirit of circular economy is the concept of "Zero Waste". The example used
in our daily lives means 100% of waste treatment, leaving no trace. At this time, it would be an ideal goal that
the waste could be fully recovered into available raw materials or energies. In particular, "waste-to-energy" is
a key factor, because all the wastes are almost related to energy. Resource recycling of waste metal from the
household garbage is the best example. When smelting metals, the refining industry needs to reduce the metal
oxides (mineral materials) to metals, such as steel, aluminium, copper, etc. The reduction processes consume
considerable portion of energy for the entire smelting process, for example, 70.6% for steel and 77.4% for
aluminium. However, if the waste metallic products can be fully recovered, as long as by melting and reshaping,
the original oxide metal reduction processes that consume a lot of energy can be avoided. On the other hand,
when the general garbage cannot be recovered as a resource, they can be converted into fuel or electricity by
biological or thermal treatment. Another more important human waste utilization is the waste paper recycling.
The production of one tonne of raw pulp emits about 6 tonnes of carbon, consuming about 100 cubic meters of
water, using about 200 kilograms of chemical raw materials, and draining 300 tonnes of toxic waste water. The
entire papermaking process is how terrible environmental pollution! The recycled pulp of one tonne can save
energy 10-13GJ.The proportion of paper waste in Taiwan 2015 is 34.69% and the estimated amount is 2.5
million tonnes. If the paper waste could be fully recycled, it could save energy about 0.725 million kloe (kilolitre oil equivalent). In other words, it virtually reduces Taiwan's oil imports of 4.56 million barrels and CO2
emissions of 2.5 million tonnes annually.
Survivin Immunoreactivity in the Gastric Mucosa of Rats Feedind with Carpet S...IJRTEMJOURNAL
Survivin has been studied many times because of its overexpression in several types of cancer
including lung, kidney, skin, endometrium, stomach, colon, breast, prostate, over, hematologic, head and neck
cancers, histopathology features and polymorphisms in the promoter region which belongs to the inhibitör of
apoptosis gene family by researchers. There is no study of survivin immunoreactivity in the gastric mucosa of the
rats fed with carpet shell clam grown in the Dardanelles. In this study, it was aimed to investigate the effects of
carpet shell clam fed rats on survivin production in the gastric mucosa. The carpet shell clam given as food to the
rats were removed from the Dardanelles Çardak region. Four groups of rats are included in the study, group 1
(n=6), control group fed with standard rat food, group 2 (n=6), 75% carpet shell clam and 25% standard rat
food daily, group 3 (n=6), 75% carpet shell clam and 25% standard rat food every two days, group 4 (n=6), 75%
carpet shell clam and 25% standard rat food every three days. To detect survivin localization in the tissues, the
LAB-SA Detection System was used. Survivin immunoreactivity was detected of epithelial cells in the gastric
mucosa of rats fed with carpet shell clam. After the immunohistochemical staining processing all gastric tissue
samples are evaluated in terms of survivin immunoreactivity with light microscopy and image analysis software.
Survivin immunoreactivity was detected 0% in the first group, 83.33% in the second group, 61.83% in the third
group and 32.67% in the fourth group. There was statistically significant difference between the survivin
immunoreactivity in the gastric gland cells of the rats in the experimental and control groups (p> 0.05). Survivin
production in the gastric mucosa of rats suggests that consumption of carpet shell clam may cause tissue damage.
Security and Crime Management in University Libraries in NigeriaIJRTEMJOURNAL
Security and prevention of crime in university library is very paramount duties of librarian. The
survival of a library depends to a large extend on how secured its collections are, security of library resources
constitutes serious challenge facing university libraries in Nigeria. The paper, therefore, investigates security and
crime management in university libraries in Nigeria using university of Jos and university of Ilorin libraries. The
study adopted a descriptive survey method. The population of the study comprised 108 library personnel and
16,012 registered library users in two university libraries. While the sample size consisted of all the 108 library
personnel, and 2% of the registered users to make a total of 428 respondents. Questionnaire and interview with
the university librarians of the selected university libraries were the instruments used for data collection. Data
were analysed using frequency distribution and percentages. Results revealed that security breaches included
stealing/theft of library materials, mutilation of library materials, and non-return of borrowed items. It also
showed inadequate funding, selfish interest of the culprits and lack of institutional security policy in the library.
Base on the findings, that staff security training, electronic security system should be introduced and improve
funding of university libraries among others. Recommendation orientation of users and staff should be done from
time to time in university libraries to mention but few.
Influence of heat treatment on Vitamin C Levels in Oyster MushroomIJRTEMJOURNAL
The study was conducted to investigate the influence of heat treatment during drying process of
Oyster mushroom in the tropics. Mushroom growing is carried out under carefully controlled conditions mostly
in bulk in specific designed tunnels with aerated floors. There are two main purposes, firstly pasteurization; to
free the compost from undesirable microbes and pests and secondly conditioning; to become mushroom specific
by getting clear of ammonia and free of readily available carbohydrates. Through proper manipulation of
temperature and ventilation these two primary objectives are accomplished. Mushrooms have been identified as
an underutilized crop in Africa, with many nutritive and health benefits. It does not require much land and
investment. However, it is highly perishable and there is need to process it to lengthen its shelf life by drying.
However, there is need to ensure that the nutrients are not lost in the process. It is for this reason that this
project investigated the effect of drying on nutrient levels in mushroom. Vitamin C levels were monitored in the
course of drying at 80⁰C, 60⁰C, 50⁰C, 40⁰C and in direct sunlight. It was concluded that the temperature that
gave the best drying rate with minimal nutrient loss was 60⁰C. In general, more than half the Vitamin C is lost
during the range of drying temperatures investigated.
Optimization of Design Parameters for Crane Hook Using Finite Element AnalysisIJRTEMJOURNAL
The Crane hooks are very at risk segments that are regularly utilized for mechanical purposes.
In this way such segments in an industry must be produced and composed in an approach to convey most extreme
execution without failure. Failure of a crane hook essentially relies upon three central point i.e. measurement,
material, overload. The undertaking is worried towards expanding the safe load by fluctuating the cross-sectional
measurements of the four distinct segments and diverse materials. The chose areas are square, circle, and
trapezoidal. The territory stays consistent while changing the measurements of the four unique segments. The
crane hook is demonstrated utilizing catia programming. The pressure and life investigation is finished utilizing
ANSYS 18.1 workbench. The ordinary worry along add up to misshaping, stress and life’s according to the
materials considered. It is discovered that trapezoidal cross segment yields most extreme load of 4000 KG to 5000
KG for steady cross segment zone among four cross segment.
Macroeconomic stability in the DRC: highlighting the role of exchange rate an...IJRTEMJOURNAL
This study is part of a macroeconomic approach and seeks to identify the role of the rate of
economic growth and the exchange rate in controlling the macroeconomic framework. The approaches adopted
in this paper are part of Keynesian thinking on macroeconomic stability using the macroeconomic stability
index proposed by Burnside and Dollars (2004) and A. Amine (2005). Our results argue that economic growth
is causing macroeconomic stability and that the exchange rate is negatively and significantly accounting for
macroeconomic stability in the Democratic Republic of Congo.
Reserves Estimating Carbon in Forest City District Village Bongohulawa GorontaloIJRTEMJOURNAL
The estimation of Carbon stock and carbon sink in the City Forest of Bongohulawa village,
Regency of Gorontalo (Guided by. The research was aimed to know volume growth of trees planted in the
Village District Bongohulawa Gorontalo, to calculate the volume and content of carbon biomass in the city
forest and green line (left-right path) and average carbon sequestration/tree/species. Research was conducted
in village of Bongohulawa during 4 (fourth) month; started from March until June 2011. The execution of data
collecting [of] was performed within this research area-location through observation and measurement of trees
and forest stand. For green line research area 100% inventory was upllied and for City Forest line plot
sampling was implemented. For city forest sample plots measurement was conducted in 10 sample units (each
unit sampling of 0.25 ha). Tree diameter, tree hight (total and commercial hight) and crown diameter of all tree
species within research line (green line) and research plots (city forest) was measured. Based on the research
data and its calculation, the results show that: Casuarina junghuhiana can store more carbon than other tree
species. From the inventory conducted in 3 km of green line along the road (6 meters width observations) of the
village Bongohulawa, 366 trees (consist of 7 tree species) were measured. Those tree species namely Casuarina
junghuiana 102 trees, sandalwood 46 trees, mango 7 trees, jackfruit 6 trees, Albizia 1 tree, mahogany 202 trees,
headland 2 trees. Crown cover of those tree species is 3032.54 m2
. The result of calculation also indicated that
Casuarina has higher carbon stock than other tree species that is 33.56 tons (equal with 52% of total crbon
stock). Further calculation indicated that during the period of 19 years (since 1992) Casuarina can strocked
carbon average of 1.77 tons/year. The average diameter increment of individual Casuarina tree species is about
1.72 cm/year. Furthermore, for Swietenia magrophilla King, with an average diameter increment of 1.40
cm/year, the leaves of this tree species can absorbed carbon of 18.1233 tons within green line of both sides of
the road. For research plots within City Forest which located in the valey the results of the research show that
the crown cover of 124 trees is about 1,359.67 m2, then carbon absorbtion is about 0.15 ton/tree or about 7.8
kg/tree/year. Within the research area of City Forest (located both in the valey and hill) totally there are 1,353
trees (consist of 13 tree species) and carbon absorption of the canopy is about 25.521 tons. Further calculation
results also indicated that carbon absorption of small trees ( poles) is about 25.521 tons and for sapling is
about 78.163 tons or 39,0815 tons/ha then fionally for mature trees is about 39.813 tons or 15,925 tons/ha.
An Analysis of Tourism Competitiveness Index of Europe and Caucasus: A Study ...IJRTEMJOURNAL
This study aims to find the association-ship between the Regional Rank of the Travel and
Tourism Competitiveness Index and its Indicators in 37 European countries. The cross-sectional data of the 37
European countries are collected from the World Economic Forum report- 2015. The statistical software
package, SPSS v. 20.0 is used to analyze the data. ANOVA (Analysis of Variance), Multi-co-linearity, Multiple
Regression, and Residual Analysis are the tools used to analyze to achieve out the objective of the study. RR:
Regional Rank of the Travel and Tourism Competitiveness Index is used as the dependent variable and TI:
Tourism Services Infrastructure, GP: Ground & Port Infrastructure, BE: Business Environment, PT:
Prioritization of Travel and Tourism, and CR: Cultural resources & business travel are used as the independent
variables. It is found that there is an inverse relationship between the dependent variable and all the
independent variables along with the statistical significance. It is recommended that the governments of the
European countries and the respective agents of these countries should be made aware of learning the findings
of this study to promote their countries which can be victorious in lowering their Regional Rank of the Travel
and Tourism Competitiveness Index.
Translation Errors of Public Signs in English Subtitle: Residents’ Poor Forei...IJRTEMJOURNAL
China is in an Age of Economy Thriving and Technology Advancing. This strike
increasing international visitors. In the foreigners, very few of them are able to communicate in
Chinese, which means that it is significant to provide accurate information to the foreign friends by
their understandable codes. For instance, in a hotel, a foreigner needs to know which way to go for
their daily activities without enquiring at the reception desk. These requirements are served by public
signs, e.g. the location of a canteen. Actually, this service is a challenge of Chinese people’s English
level. In recent years, as a lack of contextually linguistic and cultural knowledge, there are some
errors of translation on public English signs, resulting in some inconvenience to the oversea
travelers. This paper will analyses these problems in root and then advance prospective resolutions.
What are the determinants of the non-reimbursement for SMEs in Central Africa...IJRTEMJOURNAL
This article aims to determine the factors that are the cause of the non-repayment of credits
received from financial institutions by Cameroonian SMEs. This choice is sometimes. This non-repayment is
often caused by factors related to the environment and the functioning of SMEs. It aims to analyze and highlight
the factors that put Cameroonian SMEs in a situation of inability to repay the receivables received from
financial institutions. To achieve this goal, we opted for a mixed approach: Inductive (exploration on the
ground) and hypothetico deductive. To do this, we first analyzed the content of the interviews conducted with 15
SME managers and owners and tested data collected from a questionnaire administered face-to-face with 185
Cameroonian SMEs. . We used descriptive analysis and explanatory analysis. Our results show that the tax rate,
the mismanagement of managers, poor accounting and unforeseen situations have a significant positive
influence on the non-repayment of loans, while the age and size of SMEs exert significant negative influence on
the non-repayment of loans by Cameroonian SMEs.
Multivariate regression methods with infrared spectroscopy to detect the fals...IJRTEMJOURNAL
Recently, food safety and guaranteed of food marks have become more important subjects of
foodstuff production and the marketing of processed foods. This paper demonstrates the ability of Mid Infrared
spectroscopy coupled with multivariate regression tools to detect vegetable butter (as adulterant) in a binary
mixture with traditional cow’s butter. Blends of traditional cow’s butter with different percentages of vegetable
butter were measured using Attenuated Total Reflectance-Fourier Transform Mid Infrared Spectroscopy (ATRFTMIR). Spectral and reference data were firstly analyzed by principal component analysis (PCA) to check
outliers samples; and improve the robustness of the prediction models to be established. Multivariate regression
methods as Principal component regression (PCR) and Partial least square regression (PLSR) were used to
establish calibration model. Excellent correlation between ATR-FTMIR analysis and studied butter blends was
obtained R2 = 0.99; with Root Mean Square Errors of Prediction < 3.04, Limit of Detection 9.12% (By PCR)
and 6.06% (by PLSR), and Relative Prediction Errors as low as 3.13.
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.
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.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
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.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
The Internet of Things (IoT) is a revolutionary concept that connects everyday objects and devices to the internet, enabling them to communicate, collect, and exchange data. Imagine a world where your refrigerator notifies you when you’re running low on groceries, or streetlights adjust their brightness based on traffic patterns – that’s the power of IoT. In essence, IoT transforms ordinary objects into smart, interconnected devices, creating a network of endless possibilities.
Here is a blog on the role of electrical and electronics engineers in IOT. Let's dig in!!!!
For more such content visit: https://nttftrg.com/
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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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.
Stock market prediction using Twitter sentiment analysis
1. Invention Journal of Research Technology in Engineering & Management (IJRTEM)
ISSN: 2455-3689
www.ijrtem.com Volume 2 Issue 1 ǁ January. 2018 ǁ PP 01-04
| Volume 2 | Issue 1 | www.ijrtem.com | 1 |
Stock market prediction using Twitter sentiment analysis
Ajla Kirlić1
, Zeynep Orhan2
, Aldin Hasovic3
, Merve Kevser-Gokgol4
1 (
American Univeristy in Bosnia and Herzegovina, Sarajevo, Bosnia and Herzegovina)
2 (
BHANSA-BiH air navigation service agency, Sarajevo, Bosnia and Herzegovina)
4,2(
International Burch University, Sarajevo, Bosnia and Herzegovina)
ABSTRACT : In a study, it was investigated relationship among stock market movement and Tweeter feed
content. We are expecting to see if there is connection among sentiment information extracted from the Tweets
using a Vader in predicting movements of stock prices. As a result it was obtained strong positive correlation with
a coefficient of correlation to be 0.7815.
KEYWORDS : correlation, financial market, polarity, sentiment analysis, tweets
I. INTRODUCTION
With development of social media, public opinion becomes abundant. Social media is excellent platform for
sharing emotions publicly about any subject and as platform has important effect on public opinion. In recent
years twitter as a social media become interesting for researchers. As real time information, connects users and
inform them about subjects that are interested in. Users need to follow others to receive constant information and
updates. It is a great source of data since users every day post more than 200 million tweets and maximum size of
tweet is 140 characters [1]. There are around 50 million users of tweets, and motives for using that social media
differ from user to user: some heir users use it to stay informed, connected to other users or to increase their
popularity and awareness. Since limited number of characters to be followed tweet needs to be easy to understand
and concise. Single tweet may not look valuable but aggregated tweets analyzed can provide appreciated insight
of sentiment and public opinion [2]. Stock market prediction was always challenging as a study, and previous
researches were based on historical market prices. Well known efficient market hypothesis (EMH) find that
prediction of market significantly depend on contemporary events, product releases and news [3] Since news and
contemporary events are unpredictable was proven that market prices follow an arbitrary walk pattern with more
than 50% precision [4]. According to behavioral economics people are not rational as customers and decisions are
significantly affected by emotions and other people opinion. Getting public sentiment by retrieving online
information from Tweeter can be very valuable on market trading. If aggregated tweets about certain companies
are used and correlated with economic indicators referring to financial market, it is expected to get interesting
information. In this paper we are hoping to collect tweets related to the Microsoft Company and stock prices for
the same period of time, then decide the polarity of tweets and check correlation for the tweets and stock prices.
II. RELATED WORK
In this field there are many high-quality papers, but well-known publication is from Bollen [5]. In the study Bollen
was doing correlation among Dow Jones Industrial index (DJIA) and sentiment derived from the Tweets.
Methodology used for prediction was Fuzzy neural networks. As outcome was found that there is strongly
correlation among DJI and sentiment of Tweets. Remarkable study was performed by Chen and Lazer [6] where
they were stemming strategies of investing. On the other hand researcher Zhang [7] found that there is no
correlation among some states of mood and DJIA and [8] found high predictability of Tweets related to finance,
IT to the prices on stock. Pearson correlation coefficient was used in a research of Brian et al. [9] where stock
increase was investigated with public sentiment. In a research of Wysocki [10] was obtained around 3000
messages related to the stock, and it was tried to find correlation between volume and quality of messages with
changes in stock prices. As outcome was found high correlation between volume of messages and next day trading
in a stock. It was proven increase in tenfold during the night like 15.7 percent and that leads to 0.6 percent increase
in next day stock prices [9]. Similar to Wysocki research, in a research of Antweiler et al. [11] were taken stock
connected messages from board and it was measured how effect on stock prices. In a research was obtained around
1.4 million stock related messages from around 50 companies and on them was applied sentiment analysis and
text classification with a goal to determine sentiment of each message. As a result was proven strong positive
correlation between messages and stock prices. Previous two studies were including analyzing board stock
messages and their effect on stock prices, but there are many studies which are including social media platforms
as a source of messages and information, like Tweeter, Facebook and other social media. Pak et al. in their study
[12] used Tweeter messages for sentiment analysis and they explained methodology of processing and collecting
2. Stock market prediction using Tweeter…
| Volume 2 | Issue 1 | www.ijrtem.com | 2 |
tweets. In the research, training set was formed by using emoticons as a set for classification of sentiment, and
tweets were condensed manually. In a paper of Mittal et al. [13] was proven mechanism of predicting with
accuracy rate around 75 percent with a usage Fuzzy neural networks on DJIA and Tweets. It was created random
word questionnaire to help analyzing sentiment of tweets. Furthermore in a research [14] were collected only
tweets that are connected to the stock exchange concentrating on the top 100 stocks. Idea was to examine
correlation among sentiment of tweets and stock volume or price. It was decided to follow dollar nomenclature in
order to decrease noise in tweets. This way of nomenclature allowed to gather only tweets connected to stock
exchange market. As a result was obtained tweet correlation with prices on stock [14]. Study of Vu et al. [15]
inputted classifier of decision tree to sentiment of tweets, in order to determine movement of stock prices for four
NASDAQ companies where average accuracy was 76 percent divided as 77 percent on AAPL, 77 percent on
GOOG, 69 percent on MSFT, 85 percent on AMZN during the period of 60 days. On the other hand research of
[16] used Bayesian classifier to predict stock movement during the 55 days and looking for connection with tweet
sentiments. Srivastava and Rao [17] found relationship among financial market like stock prices and tweet
volume. Researchers proved that tweet mood has a great influence on financial market [17].
It was found substantial indication among stock return and tweets related to the certain companies [18] and it was
observed that change in stock return indicates increased numbers of posts. Interesting study was conducted by
[19] were market forecasting was made from quarterly earnings. For study was used large training set which
includes historical instability organized with n-gram topographies. Conclusions of the study indicated that with
large sets of data together with n-gram and word filtering it is noticed improvement of historical starting point.
Additionally it is noticed that POS adjective tag and handpicked word topographies improved historical starting
point. In previous papers is offered useful overview of sentiment analysis techniques and ability to connect them
with stock exchange market. As we can notice, outcomes of researches differ from twitter filtering, preprocessing
and accuracy of sentiment classifier.
III. METHODOLOGY AND RESULTS
Tweets were collected for over the period from October 2th
, 2017 to October 24th
, 2017 from Microsoft Company
extracted from Twitter API. In total were collected 22525 tweets. Stock prices were collected for the same period
of time, but it is known that stock is closed for holidays and weekends, in order to deal with the missing stock
values, it was used methodology of Goel [13]. Mostly stock prices are having a shape of concave function. Let’s
say that stock value for a day is “a” and the next day is “b” value with missing values in between. Using calculation
like (a+b)/2 it is possible to approximate missing values that we have in a stock prices.First step is preprocessing
of Tweeter data. To decide polarity of tweets it is used Vader [20] and by polarity we mean decision weather tweet
is positive, negative or neutral. Tweets that have score smaller than 0 is decided to be negative, for the ones that
have score higher than 0 was decided to be negative and the ones that have score 0 have neural polarity. For the
In Table 1 is example of tweets related to Microsoft:
Table1. Samples of collected tweets and their Vader scores
Text of tweet Timestamp
Vader
Score
Polarity
And Microsoft shareholders will each receive equity in the purchaser
2017-10-13
20:30:39
0 neutral
The smartphone is eventually going to die, and Apple, Google,
Microsoft, and Facebook are racing to kill it
2017-10-13
20:30:36
-0,8625 negative
Empower your business. Find out how Microsoft solutions can help you
work at anytime, anywhere
2017-10-07
18:00:48
5,38338 positive
Think Microsoft’s Cloud based Office Solution is just about using Word
and Excel in the cloud
2017-10-09
07:30:59
0,68471 positive
I thought I knew of all the bugs in Microsoft Excel's CSV parsing, broken
by design, but this attack vector is mad
2017-10-10
11:03:22
-12,1404 negative
Microsoft AI for Earth - Using AI to advance sustainability
2017-10-11
14:32:58
0 neutral
Microsoft has been fussy about continuing to sign on for this kind of
thing. My big fear is that
2017-10-12
18:13:37
-0,4939 negative
Thank you #Microsoft, @RepKevinYoder, and @SenMikeLee
2017-10-08
06:01:53
0,97524 positive
The latest Microsoft Education&Onenote Daily!
2017-10-06
23:47:02
0 neutral
3. Stock market prediction using Tweeter…
| Volume 2 | Issue 1 | www.ijrtem.com | 3 |
When tweets were collected and their polarity decided, next step was to collect data from stock exchange market.
Data was collected from this website Nasdaq (http://www.nasdaq.com/symbol/msft/historical). Samples of the
data you can see in the Table 2:
Table 2. Samples of stock exchange market data
Open High Low Close Adj Close Volume Date
75,67 76,03 75,54 76 75,62022 13959800 2017-10-06
75,97 76,55 75,86 76,29 75,90878 11386500 2017-10-09
76,33 76,63 76,14 76,29 75,90878 13944500 2017-10-10
76,36 76,46 75,95 76,42 76,03812 15388900 2017-10-11
76,49 77,29 76,37 77,12 76,73463 16876500 2017-10-12
77,59 77,87 77,29 77,49 77,10278 15335700 2017-10-13
In Figure 1 you can see distribution of Microsoft stock prices when market was open and closed:
Figure 1: Representation of Microsoft stock prices distribution
Afterwards we aggregated scores of Vader on tweets each day and those values correlated with stock price values.
It is known that with correlation it is possible to determine connection among two varibles. In Figure 2 we can
obtain distribution of X and Y values:
Figure 2: Distribution of X and Y values
71
72
73
74
75
76
77
78
79
80
2017-10-02
2017-10-03
2017-10-04
2017-10-05
2017-10-06
2017-10-09
2017-10-10
2017-10-11
2017-10-12
2017-10-13
2017-10-16
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4. Stock market prediction using Tweeter…
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As a result it is obtained correlation coefficient to be r=0.7815, which means that there is strong positive
correlation among stock exchange prices and tweet’s polarity for the same period of time. Strong positive
correlation means that with an increase on one variable, other variable is increased too and vice versa. Additionally
was calculated coefficient of determination to be 0.6107.
IV. CONCLUSION AND FUTURE WORK
The study found strong positive correlation among sentiment of tweets related to the Microsoft Company and
Microsoft’s stock prices. Even though there are many research papers related to sentiment analysis and predicting
stock prices we have expectation that our research will make contribution in the field of research. Our study is
making impact to data detection in terms of comparative study of sentiment analysis, determination of polarity
and correlation to the stock prices. Although some limitations in our research like giving weights to the Vader
sentiment analysis is alleged that results were showing affection of Tweeter public opinion to the stock exchange
market and movements of the stock prices. As a future work we are expecting to examine how polarity of news
is having effect on stock price movements and to obtain what has more impact to the financial market, Tweets or
news related to financial field. Moreover it is believed that in future work if weight Vader scores differently that
we can observe and get the other insight of the research.
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