basic sentiment analysis

app.py

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+import gradio as gr 
+import pandas as pd 
+import numpy as np 
+import matplotlib.pyplot as plt
+import plotly.express as px
+from stop_words import get_stop_words
+from wordcloud import WordCloud
+from datasets import load_dataset
+
+
+## import data 
+
+dataset = load_dataset("Santarabantoosoo/italian_long_covid_tweets")
+data = pd.DataFrame.from_dict(dataset["train"])
+
+# formulate a wordcloud for each emotion
+
+stop = get_stop_words('italian')
+
+# Wordcloud with anger tweets
+angry_tweets = data['tweet'][data["emotion"] == 'anger']
+stop_words = ["https", "co", "RT"] + list(stop)
+anger_wordcloud = WordCloud(max_font_size=50, max_words=50, background_color="white", stopwords = stop_words).generate(str(angry_tweets))
+
+# Wordcloud with sad tweets
+sad_tweets = data['tweet'][data["emotion"] == 'sadness']
+stop_words = ["https", "co", "RT"] + list(stop)
+sad_wordcloud = WordCloud(max_font_size=50, max_words=50, background_color="white", stopwords = stop_words).generate(str(sad_tweets))
+
+ # Wordcloud with joy tweets
+joy_tweets = data['tweet'][data["emotion"] == 'joy']
+stop_words = ["https", "co", "RT"] + list(stop)
+joy_wordcloud = WordCloud(max_font_size=50, max_words=50, background_color="white", stopwords = stop_words).generate(str(joy_tweets))
+
+
+ # Wordcloud with fear tweets
+fear_tweets = data['tweet'][data["emotion"] == 'fear']
+stop_words = ["https", "co", "RT"] + list(stop)
+fear_wordcloud = WordCloud(max_font_size=50, max_words=50, background_color="white", stopwords = stop_words).generate(str(fear_tweets))
+
+# combine wordclouds in a single matplotlib figure
+
+wc_fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2)
+
+wc_fig.tight_layout()
+
+ax1.imshow(sad_wordcloud, interpolation="bilinear")
+
+ax1.axis("off")
+
+ax1.set_title('Sadness', {'fontsize': 30})
+
+
+ax2.imshow(joy_wordcloud, interpolation="bilinear")
+
+ax2.axis("off")
+
+ax2.set_title('Joy', {'fontsize': 30})
+
+
+ax3.imshow(fear_wordcloud, interpolation="bilinear")
+
+ax3.axis("off")
+
+ax3.set_title('Fear', {'fontsize': 30})
+
+
+ax4.imshow(anger_wordcloud, interpolation="bilinear")
+
+ax4.axis("off")
+
+ax4.set_title('Anger', {'fontsize': 30})
+
+
+plt.show()
+
+# plot a pie plot for emotions' distribution 
+
+number_tweets_per_day = data.groupby(['date', 'emotion']).agg({'id': 'count'}).reset_index()
+
+number_tweets_per_day["tweet_date"] = pd.to_datetime(number_tweets_per_day["date"])
+
+time_fig = px.line(number_tweets_per_day, x = 'tweet_date', y = 'id', labels = {'id': 'count'}, color = 'emotion', 
+                  color_discrete_sequence=px.colors.qualitative.G10)
+
+# create a lineplot for emotions 
+
+sentiment_counts = data.groupby('emotion').agg({'id' : 'size'}).reset_index()
+sentiment_counts.rename(columns = {'id':'count'}, inplace = True)
+sent_fig = px.pie(sentiment_counts, values='count', names='emotion', title='Tweets within each emotion', labels = {'id': 'count'}, 
+                 color_discrete_sequence=px.colors.qualitative.G10)
+sent_fig
+
+
+def display_plot(image_choice):
+    
+    if image_choice == 'Sentiment distribution':
+        return sent_fig
+    
+    elif image_choice == 'Time series':
+        return time_fig
+    
+    elif image_choice == 'Word clouds':
+        return wc_fig
+    
+    
+with gr.Blocks() as demo:
+    gr.Markdown("## Choose your adventure")
+    with gr.Tabs():
+        with gr.TabItem("Sentiment analysis"):
+            text_input =  [gr.Radio(choices = ['Sentiment distribution', 'Word clouds', 'Time series'], label = 'Choose ur plot')]
+            plot_output = gr.Plot()
+            text_button = gr.Button("Submit")
+            
+            text_button.click(display_plot, inputs=text_input, outputs=plot_output)
+
+        with gr.TabItem("Word frequency"):
+            gr.Markdown("Nothing here yet")
+            
+        with gr.TabItem("Topic modeling"):
+            gr.Markdown("Nothing here yet")
+            
+            
+demo.launch();

requirements.txt

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+gradio
+pandas 
+matplotlib
+plotly
+stop_words
+wordcloud