This is a sentiment analysis tool built for analyzing .txt converted journal articles regarding DOW 30 index stocks.

==========

project
│   README.md
│   sentiment_analyzer.py    
│
└───SentimentAnalyzer
│   │   LoughranMcDonald_Negative.csv
│   │   LoughranMcDonald_Positive.csv
│   │
│   └───Articles
│       └───AAPL
│           │    aapl.txt
│       │   ...
│       │   ...
│       │   ...
|       └───WMT
│           │    wmt.txt

==========

• pip install -U textblob
• pip install ntlk
• pip install pandas

==========

Formula: (P-N) / (P+N)

Where P = count of positive coded sentiment words N = count of negative coded sentiment words

Score is not regarded, only count of words.

==========

==========

• Request user choice, select a stock from the available list in the DOW 30
• Assign source files based on user selection
• Force utf-8 encoding on article text to remove any non alphanumeric characters
• Tokenize article text, removing punctuation & assigning words as list items
• Create lists of pos/neg coded sentiments from Loughran McDonald csvs
• Remove pos/neg words from article text and add to pos/neg lists
• Display lists of found coded sentiments
• Count sum of pos/neg terms in each list
• Display total pos/neg found coded sentiments
• Calculate sentiment score using Relative Proportional Sentiment Score formula
• Display sentiment score to user

==========

• (Score)

  • (Positive coded sentiments)
  • (Negative coded sentiments)
  • (Relative Proportional Difference)

• AXP (American Express)

  • 64
  • 49
  • 0.13274336283185842

• AMGN (Amgen)

  • 56
  • 138
  • -0.422680412371134

• AAPL (Apple)

  • 72
  • 123
  • -0.26153846153846155

• BA (Boeing Co)

  • 21
  • 85
  • -0.6037735849056604

• CAT (Caterpillar)

  • 38
  • 33
  • 0.07042253521126761

• CSCO (Cisco Systems)

  • 129
  • 77
  • 0.2524271844660194

• CVX (Chevron)

  • 26
  • 75
  • -0.48514851485148514

• GS (Goldman Sachs Grp)

  • 40
  • 121
  • -0.5031055900621118

• HD (Home Depot)

  • 112
  • 223
  • -0.33134328358208953

• HON (Honeywell Intnl)

  • 20
  • 123
  • -0.7202797202797203

• IBM (IBM Corp)

  • 26
  • 24
  • 0.04

• INTC (Intel Corp)

  • 24
  • 64
  • -0.45454545454545453

• JNJ (Johnson & Johnson)

  • 13
  • 191
  • -0.8725490196078431

• KO (Coca-Cola Co)

  • 56
  • 34
  • 0.24444444444444444

• JPM (JPMorgan Chase)

  • 35
  • 230
  • -0.7358490566037735

• MCD (McDonald's Corp)

  • 52
  • 89
  • -0.2624113475177305

• MMM (3M Co)

  • 27
  • 127
  • -0.6493506493506493

• MRK (Merck & Co)

  • 13
  • 7
  • 0.3

• MSFT (Microsoft Corp)

  • 56
  • 105
  • -0.30434782608695654

• NKE (Nike Inc)

  • 89
  • 132
  • -0.19457013574660634

• PG (Proctor & Gamble)

  • 136
  • 78
  • 0.27102803738317754

• TRV (Travelers Co)

  • 53
  • 168
  • -0.5203619909502263

• UNH (UnitedHealth Grp)

  • 45
  • 61
  • -0.1509433962264151

• CRM (Salesforce)

  • 77
  • 44
  • 0.2727272727272727

• VZ (Verizon Comm)

  • 31
  • 85
  • -0.46551724137931033

• V (Visa)

  • 37
  • 124
  • -0.5403726708074534

• WBA (Walgreens Boots)

  • 81
  • 50
  • 0.2366412213740458

• WMT (Walmart)

  • 66
  • 52
  • 0.11864406779661017

• DIS (Walt Disney Co)

  • 244
  • 129
  • 0.30831099195710454

• DOW (DOW Inc)

  • 160
  • 112
  • 0.17647058823529413