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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
auth = tweepy.OAuthHandler(st.secrets["consumer_key"], st.secrets["consumer_secret"])
auth.set_access_token(st.secrets["access_key"], st.secrets["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 = dict()
response["labels"] = [pred["label"] for pred in preds]
response["scores"] = [pred["score"] for pred in preds]
return response
def neutralise_sentiment(preds):
for i, (label, score) in enumerate(zip(preds["labels"], preds["scores"])):
if score < 0.5:
preds["labels"][i] = "neutral"
preds["scores"][i] = 1.0 - score
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"
@st.cache(allow_output_mutation=True)
def load_model():
pipe = pipeline(task="sentiment-analysis", model="bhadresh-savani/distilbert-base-uncased-emotion")
return pipe
"""
# Twitter Emotion Analyser
"""
pipe = load_model()
twitter_handle = st.sidebar.text_input("Twitter handle:", "huggingface")
twitter_count = st.sidebar.selectbox("Number of tweets:", (10, 100, 500, 1000, 3200))
if st.sidebar.button("Get tweets!"):
tweets = get_tweets(twitter_handle, twitter_count)
preds = get_sentiment(tweets["tweets"])
# neutralise_sentiment(preds)
tweets.update(preds)
# dataframe creation + preprocessing
df = pd.DataFrame(tweets)
df["timestamps"] = pd.to_datetime(df["timestamps"])
# plots
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)
# TODO: check that stacking makes sense!
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)
# tweet sample
st.markdown(df.sample(n=5).to_markdown())
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