sentiment analysis

    import matplotlib.pyplot as plt
    import seaborn as sns
    color = sns.color_palette()
    %matplotlib inline
    import plotly.offline as py
    py.init_notebook_mode(connected=True)
    import plotly.graph_objs as go
    import plotly.tools as tls
    import plotly.express as px# Product Scoresfig = px.histogram(df, x="Score")
    fig.update_traces(marker_color="turquoise",marker_line_color='rgb(8,48,107)',
                    marker_line_width=1.5)
    fig.update_layout(title_text='Product Score')
    fig.show()

    import nltk
    from nltk.corpus import stopwords# Create stopword list:
    stopwords = set(STOPWORDS)
    stopwords.update(["br", "href"])
    textt = " ".join(review for review in df.Text)
    wordcloud = WordCloud(stopwords=stopwords).generate(textt)plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis("off")
    plt.savefig('wordcloud11.png')
    plt.show()

    # assign reviews with score > 3 as positive sentiment
    # score < 3 negative sentiment
    # remove score = 3df = df[df['Score'] != 3]
    df['sentiment'] = df['Score'].apply(lambda rating : +1 if rating > 3 else -1)

    positive = df[df['sentiment'] == 1]
    negative = df[df['sentiment'] == -1]
    #positive wordcloud
    stopwords = set(STOPWORDS)
    stopwords.update(["br", "href","good","great"])
    pos = " ".join(review for review in positive.Summary)
    wordcloud2 = WordCloud(stopwords=stopwords).generate(pos)plt.imshow(wordcloud2, interpolation='bilinear')
    plt.axis("off")
    plt.show()

    df['sentimentt'] = df['sentiment'].replace({-1 : 'negative'})
    df['sentimentt'] = df['sentimentt'].replace({1 : 'positive'})
    fig = px.histogram(df, x="sentimentt")
    fig.update_traces(marker_color="indianred",marker_line_color='rgb(8,48,107)',
                    marker_line_width=1.5)
    fig.update_layout(title_text='Product Sentiment')
    fig.show()

    def remove_punctuation(text):
    final = "".join(u for u in text if u not in ("?", ".", ";", ":",  "!",'"'))
    return finaldf['Text'] = df['Text'].apply(remove_punctuation)
    df = df.dropna(subset=['Summary'])
    df['Summary'] = df['Summary'].apply(remove_punctuation)
    dfNew = df[['Summary','sentiment']]
    index = df.index
    df['random_number'] = np.random.randn(len(index))train = df[df['random_number'] <= 0.8]
    test = df[df['random_number'] > 0.8] #### count vectorization

    from sklearn.feature_extraction.text import CountVectorizer
    vectorizer = CountVectorizer(token_pattern=r'\b\w+\b')
    train_matrix = vectorizer.fit_transform(train['Summary'])
    test_matrix = vectorizer.transform(test['Summary'])

    X_train = train_matrix
    X_test = test_matrix
    y_train = train['sentiment']
    y_test = test['sentiment']

    from sklearn.linear_model import LogisticRegression
    lr = LogisticRegression()
    lr.fit(X_train,y_train)
    predictions = lr.predict(X_test)

    from sklearn.metrics import confusion_matrix,classification_report
    new = np.asarray(y_test)
    confusion_matrix(predictions,y_test)
    print(classification_report(predictions,y_test))