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import pandas as pd |
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import plotly.express as px |
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import plotly.graph_objects as go |
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import streamlit as st |
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import tweepy |
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from plotly.subplots import make_subplots |
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from transformers import pipeline |
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consumer_key = "sHz78Xj5Dl41cqfzEHVoRcaKo" |
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consumer_secret = "3y5caZfu91nmB2MNH7mDSu5Cgf5qaVRpMfbDoCPW4dU7E46k03" |
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access_key = "1116912581434695680-x359MscPSdqEcJzoIlg4jMsCZRdyNX" |
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access_secret = "wEsALFUava2TnYXWnuacrzSK4eiYfJUFLBRWPqGuMRnTz" |
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auth = tweepy.OAuthHandler(consumer_key,consumer_secret) |
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auth.set_access_token(access_key,access_secret) |
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api = tweepy.API(auth) |
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def get_tweets(username, count): |
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tweets = tweepy.Cursor( |
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api.user_timeline, |
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screen_name=username, |
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tweet_mode="extended", |
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exclude_replies=True, |
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include_rts=False, |
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).items(count) |
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tweets = list(tweets) |
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response = { |
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"tweets": [tweet.full_text.replace("\n", "").lower() for tweet in tweets], |
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"timestamps": [str(tweet.created_at) for tweet in tweets], |
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"retweets": [tweet.retweet_count for tweet in tweets], |
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"likes": [tweet.favorite_count for tweet in tweets], |
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} |
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return response |
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def get_sentiment(texts): |
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preds = pipe(texts) |
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response = dict() |
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response["labels"] = [pred["label"] for pred in preds] |
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response["scores"] = [pred["score"] for pred in preds] |
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return response |
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def neutralise_sentiment(preds): |
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for i, (label, score) in enumerate(zip(preds["labels"], preds["scores"])): |
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if score < 0.5: |
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preds["labels"][i] = "neutral" |
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preds["scores"][i] = 1.0 - score |
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def get_aggregation_period(df): |
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t_min, t_max = df["timestamps"].min(), df["timestamps"].max() |
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t_delta = t_max - t_min |
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if t_delta < pd.to_timedelta("30D"): |
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return "1D" |
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elif t_delta < pd.to_timedelta("365D"): |
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return "7D" |
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else: |
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return "30D" |
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@st.cache_data |
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def load_model(): |
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pipe = pipeline(task="sentiment-analysis", model="bhadresh-savani/distilbert-base-uncased-emotion") |
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return pipe |
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""" |
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# Twitter Emotion Analyser |
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""" |
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pipe = load_model() |
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twitter_handle = st.sidebar.text_input("Twitter handle:", "elonmusk") |
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twitter_count = st.sidebar.selectbox("Number of tweets:", (10, 30, 50, 100)) |
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if st.sidebar.button("Get tweets!"): |
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tweets = get_tweets(twitter_handle, twitter_count) |
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preds = get_sentiment(tweets["tweets"]) |
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tweets.update(preds) |
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df = pd.DataFrame(tweets) |
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df["timestamps"] = pd.to_datetime(df["timestamps"]) |
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agg_period = get_aggregation_period(df) |
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ts_sentiment = ( |
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df.groupby(["timestamps", "labels"]) |
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.count()["likes"] |
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.unstack() |
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.resample(agg_period) |
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.count() |
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.stack() |
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.reset_index() |
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) |
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ts_sentiment.columns = ["timestamp", "label", "count"] |
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fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.15) |
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for label in ts_sentiment["label"].unique(): |
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fig.add_trace( |
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go.Scatter( |
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x=ts_sentiment.query("label == @label")["timestamp"], |
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y=ts_sentiment.query("label == @label")["count"], |
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mode="lines", |
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name=label, |
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stackgroup="one", |
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hoverinfo="x+y", |
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), |
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row=1, |
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col=1, |
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) |
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likes_per_label = df.groupby("labels")["likes"].mean().reset_index() |
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fig.add_trace( |
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go.Bar( |
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x=likes_per_label["labels"], |
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y=likes_per_label["likes"], |
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showlegend=False, |
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marker_color=px.colors.qualitative.Plotly, |
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opacity=0.6, |
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), |
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row=1, |
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col=2, |
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) |
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fig.update_yaxes(title_text="Number of Tweets", row=1, col=1) |
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fig.update_yaxes(title_text="Number of Likes", row=1, col=2) |
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fig.update_layout(height=350, width=750) |
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st.plotly_chart(fig) |
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st.markdown(df.to_markdown()) |
