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| import pandas as pd | |
| import numpy as np | |
| import re | |
| import snscrape.modules.twitter as sntwitter | |
| from transformers import pipeline | |
| import plotly.express as px | |
| from sentence_transformers import SentenceTransformer | |
| def load_sentence_model(): | |
| embedding_model = SentenceTransformer('sentence_bert') | |
| return embedding_model | |
| def get_tweets(username, length=10, option = None): | |
| # Creating list to append tweet data to | |
| query = "("+username + ")"+"(to:"+username+") -filter:links filter:replies lang:id" | |
| if option == "Advanced": | |
| query = username | |
| tweets = [] | |
| # Using TwitterSearchScraper to scrape | |
| # Using TwitterSearchScraper to scrape | |
| for i,tweet in enumerate(sntwitter.TwitterSearchScraper(query).get_items()): | |
| if i>=length: | |
| break | |
| tweets.append([tweet.content]) | |
| # Creating a dataframe from the tweets list above | |
| tweets_df = pd.DataFrame(tweets, columns=["content"]) | |
| tweets_df['content'] = tweets_df['content'].str.replace('@[^\s]+','') | |
| tweets_df['content'] = tweets_df['content'].str.replace('#[^\s]+','') | |
| tweets_df['content'] = tweets_df['content'].str.replace('http\S+','') | |
| tweets_df['content'] = tweets_df['content'].str.replace('pic.twitter.com\S+','') | |
| tweets_df['content'] = tweets_df['content'].str.replace('RT','') | |
| tweets_df['content'] = tweets_df['content'].str.replace('amp','') | |
| # remove emoticon | |
| tweets_df['content'] = tweets_df['content'].str.replace('[^\w\s#@/:%.,_-]', '', flags=re.UNICODE) | |
| # remove whitespace leading & trailing | |
| tweets_df['content'] = tweets_df['content'].str.strip() | |
| # remove multiple whitespace into single whitespace | |
| tweets_df['content'] = tweets_df['content'].str.replace('\s+', ' ') | |
| # remove row with empty content | |
| tweets_df = tweets_df[tweets_df['content'] != ''] | |
| return tweets_df | |
| def get_sentiment(df): | |
| # Sentiment Analysis | |
| classifier = pipeline("sentiment-analysis",model = "indobert") | |
| df['sentiment'] = df['content'].apply(lambda x: classifier(x)[0]['label']) | |
| # change order sentiment to first column | |
| cols = df.columns.tolist() | |
| cols = cols[-1:] + cols[:-1] | |
| df = df[cols] | |
| return df | |
| def get_bar_chart(df): | |
| df= df.groupby(['sentiment']).count().reset_index() | |
| # plot barchart sentiment | |
| # plot barchart sentiment | |
| fig = px.bar(df, x="sentiment", y="content", color="sentiment",text = "content", color_discrete_map={"positif": "#00cc96", "negatif": "#ef553b","netral": "#636efa"}) | |
| # hide legend | |
| fig.update_layout(showlegend=False) | |
| # set margin top | |
| fig.update_layout(margin=dict(t=0, b=100, l=0, r=0)) | |
| # set title in center | |
| # set annotation in bar | |
| fig.update_traces(textposition='outside') | |
| fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide') | |
| # set y axis title | |
| fig.update_yaxes(title_text='Jumlah Komentar') | |
| return fig | |