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import pandas as pd | |
import plotly.express as px | |
import plotly.graph_objects as go | |
import streamlit as st | |
import tweepy | |
from plotly.subplots import make_subplots | |
from transformers import pipeline | |
# Twitter API keys (should be stored securely, e.g., in environment variables) | |
consumer_key = "your_consumer_key" | |
consumer_secret = "your_consumer_secret" | |
access_key = "your_access_key" | |
access_secret = "your_access_secret" | |
auth = tweepy.OAuthHandler(consumer_key, consumer_secret) | |
auth.set_access_token(access_key, access_secret) | |
api = tweepy.API(auth) | |
def get_tweets(username, count): | |
tweets = tweepy.Cursor(api.user_timeline, screen_name=username, tweet_mode="extended", exclude_replies=True, include_rts=False).items(count) | |
tweets = list(tweets) | |
response = { | |
"tweets": [tweet.full_text.replace("\n", "").lower() for tweet in tweets], | |
"timestamps": [str(tweet.created_at) for tweet in tweets], | |
"retweets": [tweet.retweet_count for tweet in tweets], | |
"likes": [tweet.favorite_count for tweet in tweets], | |
} | |
return response | |
def get_sentiment(texts): | |
preds = pipe(texts) | |
response = { | |
"labels": [pred["label"] for pred in preds], | |
"scores": [pred["score"] for pred in preds], | |
} | |
return response | |
def get_aggregation_period(df): | |
t_min, t_max = df["timestamps"].min(), df["timestamps"].max() | |
t_delta = t_max - t_min | |
if t_delta < pd.to_timedelta("30D"): | |
return "1D" | |
elif t_delta < pd.to_timedelta("365D"): | |
return "7D" | |
else: | |
return "30D" | |
def load_model(): | |
pipe = pipeline(task="sentiment-analysis", model="bhadresh-savani/distilbert-base-uncased-emotion") | |
return pipe | |
# Streamlit app | |
st.title("Twitter Emotion Analyser") | |
pipe = load_model() | |
twitter_handle = st.sidebar.text_input("Twitter handle:", "elonmusk") | |
twitter_count = st.sidebar.selectbox("Number of tweets:", (10, 30, 50, 100)) | |
if st.sidebar.button("Get tweets!"): | |
tweets = get_tweets(twitter_handle, twitter_count) | |
preds = get_sentiment(tweets["tweets"]) | |
tweets.update(preds) | |
df = pd.DataFrame(tweets) | |
df["timestamps"] = pd.to_datetime(df["timestamps"]) | |
agg_period = get_aggregation_period(df) | |
ts_sentiment = df.groupby(["timestamps", "labels"]).count()["likes"].unstack().resample(agg_period).count().stack().reset_index() | |
ts_sentiment.columns = ["timestamp", "label", "count"] | |
fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.15) | |
for label in ts_sentiment["label"].unique(): | |
fig.add_trace(go.Scatter(x=ts_sentiment.query("label == @label")["timestamp"], y=ts_sentiment.query("label == @label")["count"], mode="lines", name=label, stackgroup="one", hoverinfo="x+y"), row=1, col=1) | |
likes_per_label = df.groupby("labels")["likes"].mean().reset_index() | |
fig.add_trace(go.Bar(x=likes_per_label["labels"], y=likes_per_label["likes"], showlegend=False, marker_color=px.colors.qualitative.Plotly, opacity=0.6), row=1, col=2) | |
fig.update_yaxes(title_text="Number of Tweets", row=1, col=1) | |
fig.update_yaxes(title_text="Number of Likes", row=1, col=2) | |
fig.update_layout(height=350, width=750) | |
st.plotly_chart(fig) | |
st.markdown(df.to_markdown()) | |