{"id":86,"date":"2017-12-21T01:18:18","date_gmt":"2017-12-21T01:18:18","guid":{"rendered":"http:\/\/ai.intelligentonlinetools.com\/ml\/?p=86"},"modified":"2018-10-20T02:31:30","modified_gmt":"2018-10-20T02:31:30","slug":"sentiment-analysis-twitter-data","status":"publish","type":"post","link":"https:\/\/ai.intelligentonlinetools.com\/ml\/sentiment-analysis-twitter-data\/","title":{"rendered":"Sentiment Analysis of Twitter Data"},"content":{"rendered":"<div class=\"yfkrc69f235cf74bb2\" ><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.yfkrc69f235cf74bb2 {\r\nmargin: 5px; padding: 0px;\r\n}\r\n@media screen and (min-width: 1201px) {\r\n.yfkrc69f235cf74bb2 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 993px) and (max-width: 1200px) {\r\n.yfkrc69f235cf74bb2 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 769px) and (max-width: 992px) {\r\n.yfkrc69f235cf74bb2 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 768px) and (max-width: 768px) {\r\n.yfkrc69f235cf74bb2 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (max-width: 767px) {\r\n.yfkrc69f235cf74bb2 {\r\ndisplay: block;\r\n}\r\n}\r\n<\/style>\r\n<p><b>Sentiment analysis of text (or opinion mining)<\/b> allows us to extract opinion from user comments on the web. The <b>applications<\/b> of sentiment analysis can be such as understanding what customers think about product or product features, discovering user reaction on certain events.<\/p>\n<p>A basic task in sentiment analysis of text is classifying the <b>polarity<\/b> of a given text from the document. Polarity can be classified as positive, negative, or neutral. <\/p>\n<p>Advanced, &#8220;beyond polarity&#8221; sentiment classification looks at emotional states such as &#8220;angry&#8221;, &#8220;sad&#8221;, and &#8220;happy&#8221;.  [1] <\/p>\n<p>In this post you will find example how to calculate <b>polarity in sentiment analysis<\/b> for twitter data  using python. Polarity in this example will have two labels: <b>positive<\/b> or <b>negative<\/b>.<br \/>\nIn the end of this post you also will find links to several most comprehensive posts from other websites on the topic <b>twitter sentiment analysis tutorial<\/b>.  <\/p>\n<h3>Dataset for Sentiment Analysis of Twitter Data<\/h3>\n<p>We will use dataset from Twitter that can be downloaded from this link [3]  from CrowdFlower [4]. This dataset contains labels for the emotional content (such as happiness, sadness, and anger) of texts. About 40000 rows of examples across 13 labels. A subset of this data was used in an experiment for Microsoft\u2019s Cortana Intelligence Gallery.<br \/>\nThe dataset has 4 columns<br \/>\n  tweet_id<br \/>\n  sentiment (for example happy, sad )<br \/>\n  author<br \/>\n  content<\/p>\n<h3>Preprocessing of Twitter Data<\/h3>\n<p>We will remove some special characters and links using below function found on Internet. <\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">\r\nimport re\r\n# below function is based on example from \r\n# http:\/\/www.geeksforgeeks.org\/twitter-sentiment-analysis-using-python\/\r\ndef clean_tweet( tweet):\r\n        '''\r\n        Utility function to clean tweet text by removing links, special characters\r\n        using simple regex statements.\r\n        '''\r\n        tweet = tweet.lower() \r\n        return ' '.join(re.sub(&quot;(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\\/\\\/\\S+)&quot;, &quot; &quot;, tweet).split())\r\n<\/pre>\n<p>Also we remove stop words as below<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">\r\nfrom many_stop_words import get_stop_words\r\nfrom nltk.corpus import stopwords\r\nfrom nltk.tokenize import word_tokenize\r\nfrom itertools import chain\r\n\r\n\r\nfrom nltk.classify import NaiveBayesClassifier, accuracy\r\n\r\nstop_words = list(get_stop_words('en'))         #About 900 stopwords\r\nnltk_words = list(stopwords.words('english'))   #About 150 stopwords\r\nstop_words.extend(nltk_words)\r\n\r\ndef remove_stopwords(word_list):\r\n               \r\n        filtered_tweet=&quot;&quot;\r\n        for word in word_list:\r\n            word = word.lower() \r\n            if word not in stopwords.words(&quot;english&quot;):\r\n                filtered_tweet=filtered_tweet + &quot; &quot; + word\r\n        \r\n        \r\n        return filtered_tweet.lstrip()\r\n<\/pre>\n<h3>Approach for Tweet Sentiment Analysis<\/h3>\n<p>We will divide tweets data into training and testing datasets. For training classifier for detecting polarity in the content column we will use training dataset with content (X) and sentiment (Y) fields.  <\/p>\n<p>As we already have emotion column for tweets we do not need do feature selection for classification. <\/p>\n<p>However we will map emotions (13 categories) in positive negative, neutral and skip neutral. <\/p>\n<p>Here is how we do mapping in the script:<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">\r\npolarity = {'empty' : 'N',\r\n                'sadness' : 'N',\r\n                'enthusiasm' : 'P',\r\n                'neutral' : 'neutral',\r\n                'worry' : 'N',\r\n                'surprise' : 'P',\r\n                'love' : 'P',\r\n                'fun' : 'P',\r\n                'hate' : 'N',\r\n                'happiness' : 'P',\r\n                'boredom' : 'N',\r\n                'relief' : 'P',\r\n                'anger' : 'N'\r\n         }  \r\n<\/pre>\n<h3>Text Classification &#8211; Using NLTK for Sentiment Analysis<\/h3>\n<p>There are different <b>classifications techniques<\/b> that can be utilized in sentiment analysis, the detailed survey of methods was published in the paper [2]. The paper has also accuracy comparison and sentiment analysis process description.<\/p>\n<p>Our task is <b>to train classifier to detect polarity (negative, positive)<\/b> for not seen text tweets.<br \/>\nWe will use <b>NLTK NaiveBayesClassifier<\/b> algorithm.<\/p>\n<p>For <b>NLTK<\/b> we do not need to convert to numeric vectors like we do for ski-learn. We need just tokenize our text and then input to machine learning classification algorithm.<\/p>\n<p>Our <strong>vocabulary<\/strong> consists of tweet words and polarity (P or N) for each tweet. Here is how it looks:<\/p>\n<figure id=\"attachment_92\" aria-describedby=\"caption-attachment-92\" style=\"width: 690px\" class=\"wp-caption alignnone\"><img decoding=\"async\" loading=\"lazy\" src=\"http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2017\/12\/sentiment-analysis-twitter-data-vocabulary-e1513963192392.png\" alt=\"vocabulary for sentiment analysis twitter data with NLTK\" width=\"700\" height=\"185\" class=\"size-full wp-image-92\" \/><figcaption id=\"caption-attachment-92\" class=\"wp-caption-text\">vocabulary for sentiment analysis twitter data with NLTK<\/figcaption><\/figure>\n<p>From vocabulary we need to create <strong>feature set<\/strong> for Naive Bayes Classifier that we are going to use. In our model each word in the tweet is treated as the feature. Each tweet is &#8220;projected&#8221; into vocabulary and each word in vocabulary is getting value True if this word is in the given tweet, and value False if the word is not found in the vocabulary. In the end of tweet we have the label for polarity of tweet.<\/p>\n<p>Below is screenshot for feature set, the polarity label (N or P is highlighted, the vocabulary is decreased just to 10 tweets for this picture.<\/p>\n<figure id=\"attachment_94\" aria-describedby=\"caption-attachment-94\" style=\"width: 890px\" class=\"wp-caption alignnone\"><img decoding=\"async\" loading=\"lazy\" src=\"http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2017\/12\/sentiment-analysis-twitter-data-feature-set-e1513962682767.png\" alt=\"sentiment analysis twitter data - feature set\" width=\"900\" height=\"310\" class=\"size-full wp-image-94\" \/><figcaption id=\"caption-attachment-94\" class=\"wp-caption-text\">sentiment analysis twitter data &#8211; feature set<\/figcaption><\/figure>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">\r\nvocabulary = set(chain(*[word_tokenize(i[0].lower()) for i in training_data]))\r\nfeature_set = [({i:(i in word_tokenize(sentence.lower())) for i in vocabulary},tag) for sentence, tag in training_data]\r\nsize = int(len(feature_set) * 0.2)\r\ntrain_set, test_set = feature_set[size:], feature_set[:size]\r\n\r\nclassifier = NaiveBayesClassifier.train(train_set)\r\nprint(accuracy(classifier, test_set))\r\n<\/pre>\n<h3>Results of Tweet Sentiment Analysis<\/h3>\n<p>Here are the results of execution python source code described above:<br \/>\nAccuracy 73%<br \/>\nRun time was long as 50 min and data sample was limited to 1000 rows. May be because laptop has only 6GB memory.  <\/p>\n<p>So we learned how to detect negative or positive polarity for sentiment analysis in twitter data. The results are showing that some improvements still would be needed. For example we could better preprocess twitter data using transformation of twitter slang words or short form words to regular words.  <\/p>\n<p>Below you can find full python source code.<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">\r\n# sentiment analysis of text twitter data\r\nimport re\r\n\r\n\r\n# below function is based on http:\/\/www.geeksforgeeks.org\/twitter-sentiment-analysis-using-python\/\r\ndef clean_tweet( tweet):\r\n        '''\r\n        Utility function to clean tweet text by removing links, special characters\r\n        using simple regex statements.\r\n        '''\r\n        tweet = tweet.lower() \r\n        return ' '.join(re.sub(&quot;(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\\/\\\/\\S+)&quot;, &quot; &quot;, tweet).split())\r\n    \r\n\r\n# below few lines are from https:\/\/stackoverflow.com\/questions\/5486337\/how-to-remove-stop-words-using-nltk-or-python   \r\nfrom many_stop_words import get_stop_words\r\nfrom nltk.corpus import stopwords\r\nfrom nltk.tokenize import word_tokenize\r\nfrom itertools import chain\r\n\r\nfrom nltk.classify import NaiveBayesClassifier, accuracy\r\nstop_words = list(get_stop_words('en'))         #About 900 stopwords\r\nnltk_words = list(stopwords.words('english'))   #About 150 stopwords\r\nstop_words.extend(nltk_words)\r\n\r\ndef remove_stopwords(word_list):\r\n \r\n        filtered_tweet=&quot;&quot;\r\n        for word in word_list:\r\n            word = word.lower() # in case they arenet all lower cased\r\n            if word not in stopwords.words(&quot;english&quot;):\r\n                filtered_tweet=filtered_tweet + &quot; &quot; + word\r\n        \r\n        \r\n        return filtered_tweet.lstrip()\r\n    \r\n\r\nfilefolder=&quot;C:\\\\Users\\\\Downloads&quot;\r\nfilename=filefolder + &quot;\\\\text_emotion.csv&quot;\r\n   \r\npolarity = {'empty' : 'N',\r\n                'sadness' : 'N',\r\n                'enthusiasm' : 'P',\r\n                'neutral' : 'neutral',\r\n                'worry' : 'N',\r\n                'surprise' : 'P',\r\n                'love' : 'P',\r\n                'fun' : 'P',\r\n                'hate' : 'N',\r\n                'happiness' : 'P',\r\n                'boredom' : 'N',\r\n                'relief' : 'P',\r\n                'anger' : 'N'\r\n         }  \r\n   \r\ntweets = []\r\ntraining_data = []\r\nimport csv\r\nwith open(filename) as csvDataFile:\r\n    csvReader = csv.reader(csvDataFile)\r\n    count=0\r\n    for row in csvReader:\r\n      \r\n        if (row[1] == 'neutral' or row[1] == 'sentiment') :\r\n            continue\r\n        tweet= clean_tweet(row[3])\r\n        tweet = remove_stopwords(tweet.split())\r\n        tweets.append(tweet)\r\n        training_data.append([tweet,  polarity[row[1]] ])\r\n        count=count+1\r\n        if (count &gt;1000):\r\n            break\r\n        \r\nprint (training_data)\r\nvocabulary = set(chain(*[word_tokenize(i[0].lower()) for i in training_data]))\r\n\r\nfeature_set = [({i:(i in word_tokenize(sentence.lower())) for i in vocabulary},tag) for sentence, tag in training_data]\r\n\r\nsize = int(len(feature_set) * 0.2)\r\ntrain_set, test_set = feature_set[size:], feature_set[:size]\r\n\r\nclassifier = NaiveBayesClassifier.train(train_set)\r\nprint(accuracy(classifier, test_set))\r\n<\/pre>\n<h3> External Resources for Twitter Sentiment Analysis Tutorial<\/h3>\n<p><a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2018\/07\/hands-on-sentiment-analysis-dataset-python\/\" target=\"_blank\">Comprehensive Hands on Guide to Twitter Sentiment Analysis with dataset and code<br \/>\n<\/a>   The author of this article is showing how to solve the Twitter Sentiment Analysis Practice Problem.<\/p>\n<p><a href=\"https:\/\/towardsdatascience.com\/another-twitter-sentiment-analysis-bb5b01ebad90\" target=\"_blank\">Another Twitter sentiment analysis with Python\u200a\u2014\u200aPart 1<\/a>    This is post 1 of series of 11 posts all about sentiment analysis twitter python and related concepts.   The posts cover such topics like word embeddings and neural networks. Below are just 2 posts from this series.<\/p>\n<p><a href=\"https:\/\/towardsdatascience.com\/another-twitter-sentiment-analysis-with-python-part-10-neural-network-with-a6441269aa3c\" target=\"_blank\">Another Twitter sentiment analysis with Python\u200a\u2014\u200aPart 10 (Neural Network with Doc2Vec\/Word2Vec\/GloVe)<\/a><\/p>\n<p><a href=\"https:\/\/towardsdatascience.com\/another-twitter-sentiment-analysis-with-python-part-11-cnn-word2vec-41f5e28eda74\" target=\"_blank\">Another Twitter sentiment analysis with Python\u200a\u2014\u200aPart 11 (CNN + Word2Vec)<\/a><\/p>\n<p><a href=https:\/\/towardsdatascience.com\/yet-another-twitter-sentiment-analysis-part-1-tackling-class-imbalance-4d7a7f717d44 target=\"_blank\">Yet Another Twitter Sentiment Analysis Part 1\u200a\u2014\u200atackling class imbalance<\/a>  <\/p>\n<p><a href=\"https:\/\/medium.freecodecamp.org\/basic-data-analysis-on-twitter-with-python-251c2a85062e\" target=\"_blank\">Basic data analysis on Twitter with Python<\/a> &#8211; Here you will find a simple data analysis program that takes a given number of tweets, analyzes them, and displays the data in a scatter plot. The data represent how Twitter users were perceiving the bot created by author and their sentiment.<\/p>\n<p><strong>References<\/strong><br \/>\n1. <a href=https:\/\/en.wikipedia.org\/wiki\/Sentiment_analysis target=\"_blank\">Sentiment Analysis<\/a><br \/>\n2. <a href=https:\/\/pdfs.semanticscholar.org\/34e7\/03d61e29213f82e3a3e6776c17933219d63f.pdf target=\"_blank\">Analysis of Various Sentiment Classification Techniques<\/a><br \/>\n3. <a href=https:\/\/www.crowdflower.com\/wp-content\/uploads\/2016\/07\/text_emotion.csv target=\"_blank\">Emotion Dataset<\/a><br \/>\n4. <a href=https:\/\/www.crowdflower.com\/data-for-everyone\/ target=\"_blank\">Data for Everyone<\/a><\/p>\n<div class=\"rcrzk69f235cf74bf9\" ><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.rcrzk69f235cf74bf9 {\r\nmargin: 5px; padding: 0px;\r\n}\r\n@media screen and (min-width: 1201px) {\r\n.rcrzk69f235cf74bf9 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 993px) and (max-width: 1200px) {\r\n.rcrzk69f235cf74bf9 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 769px) and (max-width: 992px) {\r\n.rcrzk69f235cf74bf9 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 768px) and (max-width: 768px) {\r\n.rcrzk69f235cf74bf9 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (max-width: 767px) {\r\n.rcrzk69f235cf74bf9 {\r\ndisplay: block;\r\n}\r\n}\r\n<\/style>\r\n","protected":false},"excerpt":{"rendered":"<p>Sentiment analysis of text (or opinion mining) allows us to extract opinion from user comments on the web. The applications of sentiment analysis can be such as understanding what customers think about product or product features, discovering user reaction on certain events. A basic task in sentiment analysis of text is classifying the polarity of &#8230; <a title=\"Sentiment Analysis of Twitter Data\" class=\"read-more\" href=\"https:\/\/ai.intelligentonlinetools.com\/ml\/sentiment-analysis-twitter-data\/\" aria-label=\"More on Sentiment Analysis of Twitter Data\">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":[15],"tags":[13,14,12],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Sentiment Analysis of Twitter Data - 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=\"http:\/\/ai.intelligentonlinetools.com\/ml\/sentiment-analysis-twitter-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Sentiment Analysis of Twitter Data - Text Analytics Techniques\" \/>\n<meta property=\"og:description\" content=\"Sentiment analysis of text (or opinion mining) allows us to extract opinion from user comments on the web. 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