{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"path = '/kaggle/input/quora-insincere-questions-classification/train.csv'\ntrain = pd.read_csv(path, nrows=1000)\ntrain.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"markdown","source":"# method to convert text to numerical values\n - Doccument term matrix\n - using word2vec/Doc2vec\n \n# Text cleaning for classification\n- convert every character to lower case\n- using regular expression retain only alphabets(sometime numbers, # and @)\n- remove commonly used words\n- identify root form of the word (stemming, lemmatization)"},{"metadata":{"trusted":true},"cell_type":"code","source":"# converting every character to lowercase\ndocs = train['question_text'].str.lower()\ndocs.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Remove non alphabets\ndocs = docs.str.replace('[^a-z ]','')\ndocs.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import nltk\nstopwords = nltk.corpus.stopwords.words('english')\nstemmer = nltk.stem.PorterStemmer()\n\n# other way to do using lambda.\n# docs.apply(lambda v:v.split(' ')).head() \ndef clean_sentence(doc):\n    words = doc.split(' ')\n    words_clean = [stemmer.stem(word) for word in words if word not in stopwords]\n    return ' '.join(words_clean)\n    \ndocs = docs.apply(clean_sentence)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"![](http://)# converting alphabets into numbers - doccument trm matrix\nto normalize in text mining.\nTerm frquency(TF)\nInverse doccumnet Frequency(IDF)"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.model_selection import train_test_split\ndtm_vectorizer = CountVectorizer()\n\n\ntrain_x,validate_x, train_y,validate_y = train_test_split(docs, train['target'],test_size = 0.2,random_state = 1)\ndtm_vectorizer.fit(train_x)\ndtm_train = dtm_vectorizer.transform(train_x)\ndtm_validate = dtm_vectorizer.transform(validate_x)\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_dtm_train=pd.DataFrame(dtm_train.toarray(),columns=dtm_vectorizer.get_feature_names(),index=train_x.index)\ndf_dtm_train","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_dtm_train = pd.DataFrame(dtm_train.toarray(),columns=dtm_vectorizer.get_feature_names(),index=train_x.index)\ndf_dtm_train.sum().sort_values(ascending=False).head(20).plot.bar()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.naive_bayes import MultinomialNB\nmodel=MultinomialNB().fit(dtm_train,train_y)\ntrain_y_pred=model.predict(dtm_validate)\n\nfrom sklearn.metrics import accuracy_score,f1_score\nprint(accuracy_score(validate_y,train_y_pred))\nprint(f1_score(validate_y,train_y_pred))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from nltk.sentiment import SentimentIntensityAnalyzer\nsentiment_analyzer=SentimentIntensityAnalyzer()\nsentiment_analyzer.polarity_scores('i like india')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"markdown","source":"> Here we can see the  worrld ' I  Like India' has a positive sentiment of 0.714."},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}