Text_To_Emotion_Reviews.py

import requests
import pandas as pd

# Load dataset (first 30 reviews)
url = "https://raw.githubusercontent.com/Abhayparashar31/NLPP_sentiment-analsis-on-hotel-review/refs/heads/main/Restaurant_Reviews.tsv"
df = pd.read_csv(url, sep='\t').head(30)

# API details
API_KEY = ""
API_URL = "https://api.apilayer.com/text_to_emotion"
headers = {"apikey": API_KEY}

# Function to get dominant emotion
def get_emotion(text):
    response = requests.post(API_URL, headers=headers, data=text.encode("utf-8"))
    if response.status_code == 200:
        emotions = response.json()
        return max(emotions, key=emotions.get)  # Return the highest scoring emotion
    return "Error"

# Apply API to each review
df["Emotion"] = df["Review"].apply(get_emotion)

# Function to apply color coding based on emotion
def highlight_emotion(row):
    colors = {
        "Angry": "background-color: red; color: white;",
        "Happy": "background-color: green; color: white;",
        "Fear": "background-color: brown; color: white;",
        "Sad": "background-color: orange; color: black;",
        "Surprise": "background-color: lightblue; color: black;"
    }
    return [colors.get(row["Emotion"], "")] * len(row)

# Style DataFrame
styled_df = df[["Review", "Emotion"]].style.apply(highlight_emotion, axis=1)

# Display DataFrame with styles
styled_df