├── data ├── Goldman_sachs.csv ├── mean_polarity.csv └── sw.csv └── README.md /data/Goldman_sachs.csv: -------------------------------------------------------------------------------- 1 | date,open,close,high,low,volume 2 | 2020-03-02,201.41,209.47,209.62,197.63,5285526 3 | 2020-03-03,209.0,203.43,211.79,202.12,5677200 4 | 2020-03-04,205.84,208.74,208.92,202.7,3520352 5 | 2020-03-05,201.33,198.79,203.47,196.95,4151707 6 | 2020-03-06,190.68,192.85,196.3,189.28,5071909 7 | 2020-03-09,173.58,172.81,179.25,170.16,6443000 8 | 2020-03-10,181.62,184.35,185.4,173.29,6803867 9 | 2020-03-11,178.48,171.89,180.77,170.97,5316998 10 | 2020-03-12,160.0,150.68,166.75,150.0,8357743 11 | 2020-03-13,162.82,177.17,177.56,152.35,6368355 12 | 2020-03-16,150.23,154.66,169.54,150.0,6888003 13 | 2020-03-17,157.0,158.67,166.74,150.0,6876285 14 | 2020-03-18,150.0,140.02,155.97,135.41,9543242 15 | 2020-03-19,137.38,149.49,152.49,130.85,6548186 16 | 2020-03-20,148.99,138.41,149.19,138.29,7561254 17 | 2020-03-23,136.03,134.97,141.94,133.26,5472629 18 | 2020-03-24,144.32,153.6,153.9,143.63,6647422 19 | 2020-03-25,154.6,155.13,162.21,145.32,8587796 20 | 2020-03-26,156.37,165.79,167.66,154.31,6790307 21 | 2020-03-27,159.08,158.34,163.73,157.0,4186742 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # News-Sentiment-Analysis-in-Python 2 | This Projects attempts to analyze the daily news we are subjected to. The complete analysis consists of 2 Sections. 3 | 4 | Section 1: This section involves sentiment analysis of the news articles, We try and investigate if the sentiments associated with the news articles to answer the following questions 5 | 6 | 1. Is the Media focussing too much on the negative side of the story? Do the negative news dominate the headlines ? 7 | 2. Is there a perticular trend in the way the major publishishers from around the world report stories? 8 | 3. Do the word distribution in news articles picked up randomly follow the zipf's law? 9 | 4. Bigram Analysis of the headlines 10 | Section 2: Impact of News sentiment on stock market prices. Does it mean that if news is demoninated with negative news, the stock prices fall? 11 | 12 | API used : API Chosen: Two API's were chosen for this project, all the news articles were collected from https://newsapi.org/ . NewsAPI provides real time news articles from a wide variety of news publishers. The data for the stock prices was needed in the Section 2 of this project and hence another API was needed. The data was obtained from https://www.worldtradingdata.com/ 13 | -------------------------------------------------------------------------------- /data/mean_polarity.csv: -------------------------------------------------------------------------------- 1 | source,compound 2 | Al Jazeera English,-0.323854802259887 3 | Associated Press,0.04000793650793653 4 | BBC News,-0.24267938144329895 5 | Bloomberg,-0.34757142857142853 6 | Business Insider,-0.010395081967213119 7 | CNBC,0.15913626373626372 8 | CNN,0.11204714285714285 9 | Dailymail.co.uk,0.12641457286432156 10 | ESPN Cric Info,0.1613171052631579 11 | Economist.com,-0.006959776536312849 12 | Engadget,0.14895975609756099 13 | Entertainment Weekly,0.22983369565217385 14 | Firstpost.com,0.1257552147239264 15 | Fox News,-0.05771546391752578 16 | Hindustantimes.com,0.14539200000000008 17 | Lifehacker.com,0.2790551401869159 18 | Medical News Today,0.17398571428571433 19 | Metro.co.uk,-0.06664572864321608 20 | Mirror.co.uk,-0.03563352272727272 21 | Ndtv.com,0.24636049382716038 22 | New York Magazine,0.015939500000000006 23 | Reuters,-0.11692210526315792 24 | TechCrunch,0.26591999999999993 25 | Telegraph.co.uk,-0.18155376884422086 26 | The Hindu,-0.09144349999999994 27 | The Irish Times,-0.1967111111111111 28 | The Wall Street Journal,-0.04937969543147209 29 | The Washington Post,-0.04483451776649747 30 | Thejournal.ie,-0.11350502512562816 31 | Time,-0.10384646464646465 32 | USA Today,-0.008809638554216858 33 | Wired,0.16151 34 | -------------------------------------------------------------------------------- /data/sw.csv: -------------------------------------------------------------------------------- 1 | term 2 | the 3 | to 4 | and 5 | of 6 | a 7 | in 8 | on 9 | for 10 | s 11 | is 12 | with 13 | that 14 | at 15 | by 16 | it 17 | as 18 | from 19 | was 20 | be 21 | has 22 | an 23 | this 24 | have 25 | are 26 | not 27 | said 28 | but 29 | will 30 | he 31 | who 32 | been 33 | after 34 | their 35 | one 36 | which 37 | they 38 | his 39 | up 40 | also 41 | more 42 | all 43 | about 44 | or 45 | we 46 | out 47 | had 48 | year 49 | were 50 | new 51 | when 52 | t 53 | there 54 | two 55 | you 56 | over 57 | its 58 | can 59 | would 60 | first 61 | time 62 | last 63 | if 64 | other 65 | than 66 | i 67 | no 68 | into 69 | so 70 | some 71 | people 72 | what 73 | while 74 | just 75 | could 76 | now 77 | years 78 | like 79 | get 80 | only 81 | our 82 | them 83 | before 84 | most 85 | may 86 | being 87 | do 88 | three 89 | make 90 | any 91 | day 92 | made 93 | how 94 | back 95 | us 96 | during 97 | where 98 | 1 99 | against 100 | even 101 | because 102 | your 103 | since 104 | many 105 | off 106 | she 107 | him 108 | her 109 | take 110 | through 111 | well 112 | world 113 | told 114 | down 115 | then 116 | including 117 | state 118 | way 119 | 10 120 | president 121 | those 122 | 2 123 | under 124 | 2017 125 | old 126 | government 127 | such 128 | still 129 | between 130 | these 131 | 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