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import gradio as gr # Untuk UI
from transformers import pipeline
import pandas as pd
from torch.utils.data import Dataset, DataLoader
import torch
import gc
import re
from tqdm import tqdm
import matplotlib.pyplot as plt
import snscrape.modules.twitter as sntwitter
import datetime as dt
import sys
import os
def scrape_tweets(query, max_tweets=-1,output_path="./scraper/output/" ):
if not os.path.exists(output_path):
os.makedirs(output_path)
output_path = os.path.join(output_path,dt.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+"-"+str(query)+".csv")
tweets_list = []
if sys.version_info.minor>=8:
try:
for i,tweet in tqdm(enumerate(sntwitter.TwitterSearchScraper(query).get_items())):
if max_tweets != -1 and i >= int(max_tweets):
break
tweets_list.append([tweet.date, tweet.id, tweet.content, tweet.user.username, tweet.likeCount, tweet.retweetCount, tweet.replyCount, tweet.quoteCount, tweet.url, tweet.lang])
except KeyboardInterrupt:
print("Scraping berhenti atas permintaan pengguna")
df = pd.DataFrame(tweets_list, columns=['Datetime', 'Tweet Id', 'Text', 'Username', 'Likes', 'Retweets', 'Replies', 'Quotes', 'URL', 'Language'])
print("Tweet berbahasa Indonesia :",len(df[df["Language"] == "in"]),"/",len(tweets_list))
df = df[df["Language"] == "in"]
#Karena Google Colab menggunakan versi 3.7, library scrape yang digunakan adalah versi lawas yang tidak lengkap, sehingga kita tidak bisa melakukan filter bahasa Indonesia
else:
print("Using older version of Python")
try:
for i,tweet in tqdm(enumerate(sntwitter.TwitterSearchScraper(query).get_items())):
if max_tweets != -1 and i >= int(max_tweets):
break
tweets_list.append([tweet.date, tweet.id, tweet.content])
except KeyboardInterrupt:
print("Scraping berhenti atas permintaan pengguna")
df = pd.DataFrame(tweets_list, columns=['Datetime', 'Tweet Id', 'Text'])
df.to_csv(output_path, index=False)
print("Data tweet tersimpan di",output_path)
return df
def remove_unnecessary_char(text):
text = re.sub("\[USERNAME\]", " ", text)
text = re.sub("\[URL\]", " ", text)
text = re.sub("\[SENSITIVE-NO\]", " ", text)
text = re.sub(' +', ' ', text)
return text
def preprocess_tweet(text):
text = re.sub('\n',' ',text) # Remove every '\n'
# text = re.sub('rt',' ',text) # Remove every retweet symbol
text = re.sub('^(\@\w+ ?)+',' ',text)
text = re.sub(r'\@\w+',' ',text) # Remove every username
text = re.sub('((www\.[^\s]+)|(https?://[^\s]+)|(http?://[^\s]+))',' ',text) # Remove every URL
text = re.sub('/', ' ', text)
# text = re.sub(r'[^\w\s]', '', text)
text = re.sub(' +', ' ', text) # Remove extra spaces
return text
def remove_nonaplhanumeric(text):
text = re.sub('[^0-9a-zA-Z]+', ' ', text)
return text
def preprocess_text(text):
text = preprocess_tweet(text)
text = remove_unnecessary_char(text)
text = remove_nonaplhanumeric(text)
text = text.lower()
return text
predict = pipeline('text-classification',
model='karuniaperjuangan/smsa-distilbert-indo',
device=0 if torch.cuda.is_available() else -1)
def analyze_df_sentiment(df, batch_size):
text_list = list(df["Text"].astype(str).values)
text_list_batches = [text_list[i:i+batch_size] for i in range(0,len(text_list),batch_size)] # Memisahkan berdasar batch size dengan bantuan zip ()
predictions = []
for batch in tqdm(text_list_batches):
batch_predictions = predict(batch)
predictions += batch_predictions
df["Label"] = [pred["label"] for pred in predictions]
df["Score"] = [pred["score"] for pred in predictions]
return df
def keyword_analyzer(keyword, max_tweets, batch_size=16):
print("Scraping tweets...")
df = scrape_tweets(keyword, max_tweets=max_tweets)
df["Text"] = df["Text"].apply(preprocess_text)
print("Analyzing sentiment...")
df = analyze_df_sentiment(df, batch_size=batch_size)
fig = plt.figure()
df.groupby(["Label"])["Text"].count().plot.pie(autopct="%.1f%%", figsize=(6,6))
return fig, df[["Text", "Label", "Score"]]
with gr.Blocks() as demo:
gr.Markdown("""<h1 style="text-align:center">Aplikasi Sentiment Analysis Keyword Twitter </h1>""")
gr.Markdown(
"""
Aplikasi ini digunakan untuk melakukan sentimen analisis terhadap data di Twitter menggunakan model DistilBERT. Terdapat 2 mode yang dapat digunakan:
1. Trend/Keyword: Untuk melakukan analisis terhadap semua tweet yang mengandung keyword yang diinputkan
2. Tweet: Untuk melakukan analisis terhadap sebuah tweet yang diinputkan
"""
)
with gr.Tab("Trend/Keyword"):
gr.Markdown("""Masukkan keyword dan jumlah maksimum tweet yang ingin diambil""")
with gr.Blocks():
with gr.Row():
with gr.Column():
keyword_textbox = gr.Textbox(lines=1, label="Keyword")
max_tweets_component = gr.Number(value=-1, label="Tweet Maksimal yang akan discrape (-1 jika ingin mengscrape semua tweet)", precision=0)
batch_size_component = gr.Number(value=16, label="Batch Size (Semakin banyak semakin cepat, tetapi semakin boros memori)", precision=0)
button = gr.Button("Submit")
plot_component = gr.Plot(label="Pie Chart")
dataframe_component = gr.DataFrame(type="pandas",
label="Dataframe",
max_rows=(20,'fixed'),
overflow_row_behaviour='paginate',
wrap=True)
with gr.Tab("Single Tweet"):
gr.Interface(lambda Tweet: (predict(Tweet)[0]['label'], predict(Tweet)[0]['score']),
"textbox",
["label", "label"],
allow_flagging='never',
)
gr.Markdown(
"""
Space ini merupakan tugas NLP dari mata kuliah Pemrosesan Bahasa Alami yang diampu oleh Bapak Syukron Abu Ishaq Alfarozi.
## Anggota Kelompok
- Karunia Perjuangan Mustadl'afin - 20/456368/TK/50498
- Pramudya Kusuma Hardika - 20/460558/TK/51147
"""
)
button.click(keyword_analyzer,
inputs=[keyword_textbox, max_tweets_component, batch_size_component],
outputs=[plot_component, dataframe_component])
demo.launch(inbrowser=True) |