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d8d3197 b6ed3ee 2ef1a11 1694d0a 2ef1a11 1694d0a 2ef1a11 d8d3197 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | import pandas as pd
import re
import string
import nltk
import gradio as gr
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
# Download resources
nltk.download('stopwords')
# Load data
df = pd.read_csv("NFL_reddit_data_2021.csv")
from textblob import TextBlob
def get_sentiment(text):
if not isinstance(text, str):
text = str(text)
return "positive" if TextBlob(text).sentiment.polarity > 0 else "negative"
df["text"] = df["text"].astype("object").astype(str)
df["sentiment"] = df["text"].apply(get_sentiment)
df = df.dropna(subset=["text", "sentiment"])
# Text preprocessing
stop_words = set(stopwords.words("english"))
def clean_text(text):
text = text.lower()
text = re.sub(r"http\S+", "", text)
text = re.sub(r"\d+", "", text)
text = text.translate(str.maketrans("", "", string.punctuation))
words = text.split()
words = [w for w in words if w not in stop_words]
return " ".join(words)
df["clean_text"] = df["text"].apply(clean_text)
# Feature extraction
vectorizer = TfidfVectorizer(max_features=5000)
X = vectorizer.fit_transform(df["clean_text"])
y = df["sentiment"]
# Train model
model = LogisticRegression(max_iter=1000)
model.fit(X, y)
# Prediction function for Gradio
def predict_sentiment(user_input):
cleaned = clean_text(user_input)
vectorized = vectorizer.transform([cleaned])
prediction = model.predict(vectorized)[0]
return f"Predicted Sentiment: {prediction}"
# Gradio Interface
interface = gr.Interface(
fn=predict_sentiment,
inputs=gr.Textbox(lines=4, placeholder="Enter an NFL Reddit comment..."),
outputs="text",
title="NFL Reddit Sentiment Analyzer",
description=(
"Analyze sentiment of NFL-related Reddit comments using NLP. "
"This tool demonstrates how sentiment analysis can support NFL Draft decisions."
)
)
interface.launch()
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