Tweet analyzer web applicaion
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This tool analyzes tweets from Twitter handles by fetching the most recent tweets, generating a word cloud, and performing sentiment analysis to display results in graphs. It fetches the last 100 tweets, converts them to a dataframe, cleans the text, and analyzes polarity and subjectivity to add additional columns. Sentiment analysis determines if text is positive, negative, or neutral using natural language processing. The tool extracts and analyzes tweet data, creates wordclouds of most frequent positive, negative, and neutral words, and uses graphs like pie charts and bar graphs to visualize results.
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Twitter Sentiment Analysis.pdf
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
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What visualization are we talking about?
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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
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Twitter Sentiment Analysis.pdf
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?
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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
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Facebook has 2.2 Billion, Google+ has 111 Million and LinkedIn has 467 Million which is
enough to create an impression through social media. We have proposed a system to
determine current sentiment on twitter using Twitter API for open access which includes
opinions from different content structures like latest news, audits, articles and social media
posts. And Deep Learning method to study Historic Data for predicting future results.
Previous implementations of prediction analysis on Twitter Data were not successful to fulfill
analysis on live Twitter Data. Thus, we have proposed a system which will predict the result
on live Twitter data and also generate the statistical graph which classify the polarity of
positive and negative tweets. With the help graphs, reports, trends and tweets one can predict
the future results of the political party and also can be used to create the campaigns.
sentimentanaly 2.pdf
This document describes a sentiment analysis project that aims to analyze sentiment in tweets about electronic products using data mining techniques. The proposed system uses Naive Bayes and Support Vector Machine (SVM) algorithms to classify tweets as positive, negative, or neutral. SVM achieved higher accuracy rates than Naive Bayes. The system collects tweet data, preprocesses it by removing URLs, symbols, and more. It then trains and tests the algorithms on the preprocessed data and outputs sentiment results as a pie chart and word cloud.
Political prediction analysis using text mining and deep learning
We have proposed a system to determine current sentiment on twitter using Twit-
ter API for open access which includes opinions from di erent content structures like
latest news, audits, articles and social media posts. and Deep Learning method to
study Historic Data for predicting future results. we utilized Naive Bayes and dictio-
nary based algorithms to predict the sentiment on Live Twitter Data.
Sentiment Analysis on Twitter
This document summarizes a student's sentiment analysis project. The student analyzed tweets to determine sentiment towards certain words using tools like Spyder, Excel, Tweepy and TextBlob. The process involved getting tweet data through the Twitter API, cleaning the data by removing special characters and links, iterating through the tweets using TextBlob to analyze sentiment polarity, and visualizing the results. Next steps include expanding the analysis to different sentiments beyond just positive and negative.
Sentimental Analysis - Naive Bayes Algorithm
This document provides an overview of sentiment analysis on Twitter. It discusses how sentiment analysis can be used to determine sentiment behind texts and social media updates. The document outlines the methodology used, including data collection from Twitter, preprocessing, feature extraction, and using classifiers like Naive Bayes to predict sentiment. It also discusses applications of sentiment analysis and future areas of improvement, such as using different algorithms and temporal analysis. The goal is to more accurately analyze human sentiment from social media data.
NLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptx
NLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptxBoston Institute of Analytics
Explore the power of Natural Language Processing (NLP) and Data Science in uncovering valuable insights from Flipkart product reviews. This presentation delves into the methodology, tools, and techniques used to analyze customer sentiments, identify trends, and extract actionable intelligence from a vast sea of textual data. From understanding customer preferences to improving product offerings, discover how NLP Data Science is revolutionizing the way businesses leverage consumer feedback on Flipkart. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ Final Presentation
The document summarizes a team's sentiment analysis project on tweets mentioning JetBlue airline. It introduces the project goals of understanding sentiment patterns and predicting tweet sentiment. It describes preprocessing tweets, creating term document matrices, and using lexicon-based and machine learning approaches. Evaluation found the naive Bayesian model worked best at sentiment classification, achieving 61% accuracy. The document concludes more advanced techniques could improve analysis, like clustering tweets or integrating additional contextual variables.
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14 th Edition of International conference on computer vision
About the event
14th Edition of International conference on computer vision
Computer conferences organized by ScienceFather group. ScienceFather takes the privilege to invite speakers participants students delegates and exhibitors from across the globe to its International Conference on computer conferences to be held in the Various Beautiful cites of the world. computer conferences are a discussion of common Inventions-related issues and additionally trade information share proof thoughts and insight into advanced developments in the science inventions service system. New technology may create many materials and devices with a vast range of applications such as in Science medicine electronics biomaterials energy production and consumer products.
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一比一原版(sdsu毕业证书)圣地亚哥州立大学毕业证如何办理
原版一模一样【微信：741003700 】【(sdsu毕业证书)圣地亚哥州立大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
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【主营项目】
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二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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如何办理(hull学位证书)英国赫尔大学毕业证硕士文凭原版一模一样
原版定制【微信:bwp0011】《(hull学位证书)英国赫尔大学毕业证硕士文凭》【微信:bwp0011】成绩单 、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信bwp0011】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信bwp0011】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
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Recently uploaded (20)
14 th Edition of International conference on computer vision
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Tweet analyzer web applicaion
- 1. Tweet Analyzer Web Application Analyze the tweets of your favourite Personalities : This tool performs the following tasks : Fetches the 5 most recent tweets from the given twitter handel. 1. 2. Generates a Word Cloud 3. Performs Sentiment Analysis a displays it in form of a Bar Graph Pie-Chart,and Scatter Plot.
- 2. 3 .Analyzes Subjectivity of tweets and adds an additional column This tool fetches the last 100 tweets from the twitter handel & Performs the following tasks : 1 .Converts it into a DataFrame 2 .Cleans the text 4. Analyzes Polarity of tweets and adds an additional column 5. Analyzes Sentiments of tweets and adds an additional column
- 3. Sentiment Analysis is the process of ‘computationally’ determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mining, deriving the opinion or attitude of a speaker What Is Sentiment analysis ?
- 4. How Does Sentiment Analysis Work ? Sentiment analysis, otherwise known as opinion mining, works works thanks to natural language processing (NLP) Algorithm Used for Tweet Analyzer : Rule-based approaches usually defined a number of rules in a certain type of scripting language, which indicates subjectivity, polarity or an opinion. Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and Natural language processing (NLP) : manipulate human language.
- 5. Installation and Importing Python packages: Tweepy: tweepy is the python client for the official Twitter API. TextBlob: textblob is the python library for processing textual data. Numpy : Numpy is the python library for perform a number of mathematical operations on arrays. Pandas : Pandas is the python library used for data analysis. Matplotlib : pyplot is a plotting library used for 2D graphics in python programming language
- 6. Authentication: In order to fetch tweets through Twitter API, one needs to register an App through their twitter account. . We are going build connection with twiiter api using consmer key, consumer token , access token ,access secret Create the authentication object. Set the access token and access token secret, and authenticate to Twitter
- 7. Extract and Analyze the Data : getting the last 100 posts for the Twitter user SRK and show only the five most recent tweets
- 8. Create a DataFrame with a column called Tweets that’ll contain the posts from the Twitter user, and show the first five rows subheading
- 9. The data is a little dirty because it contains the @ symbol,hyperlinks, contain hash tags RTs, and may Cleaning of Twitter Data Cleaning of data done by regular experssion package name re.
- 10. Let’s do sentiment analysis using TextBlob. Finding both polarity and subjectivity. Calculating polarity and subjectivity Sentiment Analysis Subjectivity : how subjective or opinionated the text is — a score of 0 is fact, and a score of +1 is very much an opinion
- 12. Polarity : how positive or negative the text is, — score of -1 is the highest negative score, and a score of +1 is the highest positive score
- 13. Determining tweets is positive or negative using Data Analysis (Polarity)
- 14. Positive Tweets : percentage of positive tweets is 53 %
- 15. Negative Tweets : percentage of positive tweets is 53 %
- 16. scatter plot : used to display relationship between variables
- 17. Bar Graph : used to compare things between different groups
- 18. 53 % 37 % 10 % expresses a part-to-whole relationship in your data. Pie Chart :
- 19. Wordcloud : visual displays of text data – simple text analysis display the most prominent or frequent words in a body of text
- 20. Positive Wordcloud : Most frequently used positive words by srk
- 21. Negative Wordcloud : Most frequently used negative words by srk
- 22. Neutral Wordcloud : Most frequently used neutral words by srk
- 23. Thank You !