{"id":996,"date":"2019-10-26T20:45:22","date_gmt":"2019-10-26T20:45:22","guid":{"rendered":"http:\/\/ai.intelligentonlinetools.com\/ml\/?p=996"},"modified":"2019-11-03T15:27:09","modified_gmt":"2019-11-03T15:27:09","slug":"sentiment-analysis","status":"publish","type":"post","link":"https:\/\/ai.intelligentonlinetools.com\/ml\/sentiment-analysis\/","title":{"rendered":"Sentiment Analysis with VADER"},"content":{"rendered":"<div class=\"veiiw69dffe293733f\" ><script async src=\"\/\/pagead2.googlesyndication.com\/pagead\/js\/adsbygoogle.js\"><\/script>\n<!-- Text analytics techniques 728_90 horizontal top -->\n<ins class=\"adsbygoogle\"\n     style=\"display:inline-block;width:728px;height:90px\"\n     data-ad-client=\"ca-pub-3416618249440971\"\n     data-ad-slot=\"2926649501\"><\/ins>\n<script>\n(adsbygoogle = window.adsbygoogle || []).push({});\n<\/script><\/div><style type=\"text\/css\">\r\n.veiiw69dffe293733f {\r\nmargin: 5px; padding: 0px;\r\n}\r\n@media screen and (min-width: 1201px) {\r\n.veiiw69dffe293733f {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 993px) and (max-width: 1200px) {\r\n.veiiw69dffe293733f {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 769px) and (max-width: 992px) {\r\n.veiiw69dffe293733f {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 768px) and (max-width: 768px) {\r\n.veiiw69dffe293733f {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (max-width: 767px) {\r\n.veiiw69dffe293733f {\r\ndisplay: block;\r\n}\r\n}\r\n<\/style>\r\n<p><b>Sentiment analysis<\/b> (also known as opinion mining ) refers to the use of natural language processing, text analysis, computational linguistics to systematically identify, extract, quantify, and study affective states and subjective information. [1] In short, Sentiment analysis gives an objective idea of whether the text uses mostly positive, negative, or neutral language. [2]<\/p>\n<p>Sentiment analysis software can assist estimate people opinion on the events in finance world, generate reports for relevant information, analyze correlation between events and stock prices.<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" src=\"http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2019\/10\/smiley-2979107_640-e1572728163186.jpg\" alt=\"\" width=\"520\" height=\"292\" class=\"aligncenter size-full wp-image-1005\" \/><br \/>\n<center>Image by <a href=\"https:\/\/pixabay.com\/users\/AbsolutVision-6158753\/?utm_source=link-attribution&amp;utm_medium=referral&amp;utm_campaign=image&amp;utm_content=2979107\">Gino Crescoli<\/a> from <a href=\"https:\/\/pixabay.com\/?utm_source=link-attribution&amp;utm_medium=referral&amp;utm_campaign=image&amp;utm_content=2979107\">Pixabay<\/a><\/center><\/p>\n<h2>The problem<\/h2>\n<p>In this post we investigate how to extract information about company and detect its sentiment. For each text sentence or paragraph we will detect its positivity or negativity by calculating sentiment score. This also called polarity. <strong>Polarity<\/strong> in sentiment analysis refers to identifying sentiment orientation (positive, neutral, and negative) in the text. <\/p>\n<p>Given the list of companies we want to find polarity of sentiment in the text that has names of companies from the list. Below is the description how it can be implemented.<\/p>\n<h2>Getting Data<\/h2>\n<p>We will use google to collect data. For this we search google via script for documents with some predefined keywords.<br \/>\nIt will return the links that we will save to array.<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">\r\ntry: \r\n    from googlesearch import search \r\nexcept ImportError:  \r\n    print(&quot;No module named 'google' found&quot;) \r\n  \r\n# to search \r\nquery = &quot;financial_news Warren Buffett 2019&quot;\r\n\r\nlinks=[]  \r\nfor j in search(query, tld=&quot;co.in&quot;, num=10, stop=10, pause=6): \r\n    print(j) \r\n    links.append(j)\r\n<\/pre>\n<h2>Preprocessing<\/h2>\n<p>After we got links, we need get text documents and remove not needed text and characters. So in this step we remove html tags, not valid characters. We keep however paragraph tags. Using paragraph tag we divide text document in smaller text units. After that we remove p tags.<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">\r\npara_data=[]\r\n\r\n\r\ndef get_text(url):\r\n   print (url) \r\n   \r\n   try:\r\n      req  = requests.get(url, timeout=5)\r\n   except: \r\n      return &quot;TIMEOUT ERROR&quot;  \r\n  \r\n   data = req.text\r\n   soup = BeautifulSoup(data, &quot;html.parser&quot;)\r\n   \r\n     \r\n   paras=[]\r\n   paras_ = soup.find_all('p')\r\n   filtered_paras= filter(tag_visible, paras_)\r\n   for s in filtered_paras:\r\n       paras.append(s)\r\n   if len(paras) &gt; 0:\r\n      for i, para in enumerate(paras):\r\n           para=remove_tags(para)\r\n           # remove non text characters\r\n           para_data.append(clean_txt(para))\r\n<\/pre>\n<h2>Calculating Sentiment<\/h2>\n<p>Now we calculate sentiment score using <strong>VADER<\/strong> (Valence Aware Dictionary and sEntiment Reasoner) VADER is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments.[3] Based on calculated sentiment we build plot. In this example we only build plot for first company name which is Coca Cola.<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" src=\"http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2019\/10\/sentiment-analysis-e1572734083449.jpg\" alt=\"\" width=\"500\" height=\"394\" class=\"aligncenter size-full wp-image-1010\" \/><\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">\r\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer \r\n  \r\n\r\ndef sentiment_scores(sentence): \r\n    \r\n    # Create a SentimentIntensityAnalyzer object. \r\n    sid_obj = SentimentIntensityAnalyzer() \r\n  \r\n    # polarity_scores method of SentimentIntensityAnalyzer \r\n    # oject gives a sentiment dictionary. \r\n    # which contains pos, neg, neu, and compound scores. \r\n    sentiment_dict = sid_obj.polarity_scores(sentence) \r\n      \r\n    print(&quot;Overall sentiment dictionary is : &quot;, sentiment_dict) \r\n    print(&quot;sentence was rated as &quot;, sentiment_dict['neg']*100, &quot;% Negative&quot;) \r\n    print(&quot;sentence was rated as &quot;, sentiment_dict['neu']*100, &quot;% Neutral&quot;) \r\n    print(&quot;sentence was rated as &quot;, sentiment_dict['pos']*100, &quot;% Positive&quot;)\r\n\r\n    \r\n    # decide sentiment as positive, negative and neutral \r\n    if sentiment_dict['compound'] &gt;= 0.05 : \r\n        print(&quot;Positive&quot;) \r\n        \r\n    elif sentiment_dict['compound'] &lt;= - 0.05 : \r\n        print(&quot;Negative&quot;) \r\n  \r\n    else : \r\n        print(&quot;Neutral&quot;) \r\n    return sentiment_dict['compound'] \r\n<\/pre>\n<p>\nBelow you can find full source code.<br \/>\n<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">\r\n# -*- coding: utf-8 -*-\r\nfrom bs4 import BeautifulSoup, NavigableString\r\nfrom bs4.element import Comment\r\nimport requests\r\nimport re\r\n\r\nTAG_RE = re.compile(r'&lt;[^&gt;]+&gt;')\r\n\r\ndef remove_tags(text_string):\r\n    print (text_string)\r\n    \r\n    return TAG_RE.sub('', str(text_string))\r\n\r\nMIN_LENGTH_of_document = 40\r\nMIN_LENGTH_of_word = 2\r\n\r\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer \r\n  \r\n# function to print sentiments \r\n# of the sentence. \r\n# below function based on function from https:\/\/www.geeksforgeeks.org\/python-sentiment-analysis-using-vader\/\r\ndef sentiment_scores(sentence): \r\n    \r\n    # Create a SentimentIntensityAnalyzer object. \r\n    sid_obj = SentimentIntensityAnalyzer() \r\n  \r\n    # polarity_scores method of SentimentIntensityAnalyzer \r\n    # oject gives a sentiment dictionary. \r\n    # which contains pos, neg, neu, and compound scores. \r\n    sentiment_dict = sid_obj.polarity_scores(sentence) \r\n      \r\n    print(&quot;Overall sentiment dictionary is : &quot;, sentiment_dict) \r\n    print(&quot;sentence was rated as &quot;, sentiment_dict['neg']*100, &quot;% Negative&quot;) \r\n    print(&quot;sentence was rated as &quot;, sentiment_dict['neu']*100, &quot;% Neutral&quot;) \r\n    print(&quot;sentence was rated as &quot;, sentiment_dict['pos']*100, &quot;% Positive&quot;)\r\n    print(&quot;Sentence Overall Rated As&quot;, end = &quot; &quot;) \r\n  \r\n    return sentiment_dict['compound']    \r\n\r\ndef remove_min_words(txt):\r\n   # https:\/\/www.w3resource.com\/python-exercises\/re\/python-re-exercise-49.php\r\n   shortword = re.compile(r'\\W*\\b\\w{1,1}\\b')\r\n   return(shortword.sub('', txt))        \r\n    \r\n        \r\ndef clean_txt(text):\r\n  \r\n   text = re.sub('[^A-Za-z.  ]', ' ', str(text))\r\n   text=' '.join(text.split())\r\n   text = remove_min_words(text)\r\n   text=text.lower()\r\n   text = text if  len(text) &gt;= MIN_LENGTH_of_document else &quot;&quot;\r\n   return text\r\n        \r\n\r\ndef between(cur, end):\r\n    while cur and cur != end:\r\n        if isinstance(cur, NavigableString):\r\n            text = cur.strip()\r\n            if len(text):\r\n                yield text\r\n        cur = cur.next_element\r\n        \r\ndef next_element(elem):\r\n    while elem is not None:\r\n        # Find next element, skip NavigableString objects\r\n        elem = elem.next_sibling\r\n        if hasattr(elem, 'name'):\r\n            return elem        \r\n\r\ndef tag_visible(element):\r\n    if element.parent.name in ['style', 'script', 'head',  'meta', '[document]']:\r\n        return False\r\n    if isinstance(element, Comment):\r\n        return False\r\n    return True\r\n    \r\npara_data=[]\r\n\r\n\r\ndef get_text(url):\r\n   print (url) \r\n   \r\n   try:\r\n      req  = requests.get(url, timeout=5)\r\n   except: \r\n      return &quot;TIMEOUT ERROR&quot;  \r\n  \r\n   data = req.text\r\n   soup = BeautifulSoup(data, &quot;html.parser&quot;)\r\n   \r\n     \r\n   paras=[]\r\n   paras_ = soup.find_all('p')\r\n   filtered_paras= filter(tag_visible, paras_)\r\n   for s in filtered_paras:\r\n       paras.append(s)\r\n   if len(paras) &gt; 0:\r\n      for i, para in enumerate(paras):\r\n           para=remove_tags(para)\r\n           # remove non text characters\r\n           para_data.append(clean_txt(para))\r\n           \r\n     \r\n\r\ntry: \r\n    from googlesearch import search \r\nexcept ImportError:  \r\n    print(&quot;No module named 'google' found&quot;) \r\n  \r\n# to search \r\nquery = &quot;coca cola 2019&quot;\r\n\r\nlinks=[]  \r\nfor j in search(query, tld=&quot;co.in&quot;, num=25, stop=25, pause=6): \r\n    print(j) \r\n    links.append(j)\r\n  \r\n# Here our list consists from one company name, but it can include more than one.  \r\norgs=[&quot;coca cola&quot; ]    \r\n \r\nresults=[] \r\ncount=0  \r\n\r\n\r\ndef update_dict_value( dict, key, value):\r\n    if key in dict:\r\n           dict[key]= dict[key]+value\r\n    else:\r\n           dict[key] =value\r\n    return dict\r\n\r\nfor link in links:\r\n    # will update paras - array of paragraphs\r\n    get_text(link)\r\n    \r\n    for pr in para_data:\r\n              \r\n        for org in orgs:\r\n            if pr.find (org) &gt;=0:\r\n                # extract sentiment\r\n                score=0\r\n                results.append ([org, sentiment_scores(pr), pr])\r\n\r\n\r\npositive={}\r\nnegative={}\r\npositive_sentiment={}\r\nnegative_sentiment={}\r\n\r\n  \r\nfor i in range(len(results)):\r\n    org = results[i][0]\r\n   \r\n    if (results[i][1] &gt;=0):\r\n        positive = update_dict_value( positive, org, 1)\r\n        positive_sentiment =  update_dict_value( positive_sentiment, org,results[i][1])\r\n\r\n    else:\r\n        negative = update_dict_value( negative, org, 1)\r\n        negative_sentiment =  update_dict_value( negative_sentiment, org,results[i][1])\r\n\r\nfor org in orgs:\r\n   \r\n    positive_sentiment[org]=positive_sentiment[org] \/ positive[org]\r\n    print (negative_sentiment[org])\r\n    negative_sentiment[org]=negative_sentiment[org] \/ negative[org]   \r\n\r\nimport matplotlib.pyplot as plt \r\n\r\n\r\n# x-coordinates of left sides of bars  \r\nlabels = ['negative', 'positive'] \r\n  \r\n# heights of bars \r\nsentiment = [(-1)*negative_sentiment[orgs[0]], positive_sentiment[orgs[0]]] \r\n\r\n\r\n# labels for bars \r\ntick_label = ['negative', 'positive'] \r\n  \r\n# plotting a bar chart \r\nplt.bar(labels, sentiment, tick_label = tick_label, \r\n        width = 0.8, color = ['red', 'green']) \r\n  \r\n# naming the x-axis \r\nplt.xlabel('x - axis') \r\n# naming the y-axis \r\nplt.ylabel('y - axis') \r\n# plot title \r\nplt.title('Sentiment Analysis') \r\n  \r\n# function to show the plot \r\nplt.show() \r\n<\/pre>\n<p><strong>References<\/strong><br \/>\n1. <a href=\"https:\/\/en.wikipedia.org\/wiki\/Sentiment_analysis\" target=\"_blank\">Sentiment analysis<\/a> Wikipedia<br \/>\n2. <a href=https:\/\/readable.help\/en\/articles\/2533315-what-is-a-sentiment-score-and-how-is-it-measured target=\"_blank\">What is a &#8220;Sentiment Score&#8221; and how is it measured?<\/a><br \/>\n3. <a href=\"https:\/\/github.com\/cjhutto\/vaderSentiment\">VADER-Sentiment-Analysis<\/a><\/p>\n<div class=\"vvqor69dffe293736f\" ><center>\n<script async src=\"\/\/pagead2.googlesyndication.com\/pagead\/js\/adsbygoogle.js\"><\/script>\n<!-- Text analytics techniques link ads horizontal Medium after content -->\n<ins class=\"adsbygoogle\"\n     style=\"display:inline-block;width:468px;height:15px\"\n     data-ad-client=\"ca-pub-3416618249440971\"\n     data-ad-slot=\"5765984772\"><\/ins>\n<script>\n(adsbygoogle = window.adsbygoogle || []).push({});\n<\/script>\n\n<script async src=\"\/\/pagead2.googlesyndication.com\/pagead\/js\/adsbygoogle.js\"><\/script>\n<ins class=\"adsbygoogle\"\n     style=\"display:block\"\n     data-ad-format=\"autorelaxed\"\n     data-ad-client=\"ca-pub-3416618249440971\"\n     data-ad-slot=\"3903486841\"><\/ins>\n<script>\n     (adsbygoogle = window.adsbygoogle || []).push({});\n<\/script>\n<\/center><\/div><style type=\"text\/css\">\r\n.vvqor69dffe293736f {\r\nmargin: 5px; 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[1] In short, Sentiment analysis gives an objective idea of whether the text uses mostly positive, negative, or neutral language. [2] Sentiment analysis &#8230; <a title=\"Sentiment Analysis with VADER\" class=\"read-more\" href=\"https:\/\/ai.intelligentonlinetools.com\/ml\/sentiment-analysis\/\" aria-label=\"More on Sentiment Analysis with VADER\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"categories":[70,15],"tags":[78,77,79,80],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Sentiment Analysis with VADER - Text Analytics Techniques<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ai.intelligentonlinetools.com\/ml\/sentiment-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Sentiment Analysis with VADER - Text Analytics Techniques\" \/>\n<meta property=\"og:description\" content=\"Sentiment analysis (also known as opinion mining ) refers to the use of natural language processing, text analysis, computational linguistics to systematically identify, extract, quantify, and study affective states and subjective information. 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[1] In short, Sentiment analysis gives an objective idea of whether the text uses mostly positive, negative, or neutral language. [2] Sentiment analysis ... 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