Sentiment-Analysis/modals.py at main · Sankeerthan13/Sentiment-Analysis · GitHub

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from flair.models import TextClassifier
from flair.data import Sentence
from textblob import TextBlob
import nltk
nltk.download('vader_lexicon')
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import text2emotion as te
from fer import FER
import matplotlib.pyplot as plt
import cv2
import numpy as np

"""
    Argument:
        Single Text(String)

    Returns:
        Returns emotion(String)
"""

sia = TextClassifier.load('en-sentiment')
emo_detector = FER(mtcnn=True)

# For Text data
def flair(text):
    sentence = Sentence(text)
    sia.predict(sentence)
    score = str(sentence.labels[0])
    startIdx = int(score.rfind("("))
    endIdx = int(score.rfind(")"))
    percentage = float(score[startIdx+1:endIdx])
    if percentage < 0.60:
        return "NEUTRAL"
    elif "POSITIVE" in str(score):
        return "POSITIVE"
    elif "NEGATIVE" in str(score):
        return "NEGATIVE"


# For Text data
def textBlob(text):
    tb = TextBlob(text)
    polarity = round(tb.polarity, 2)
    if polarity>0:
        return "Positive"
    elif polarity==0:
        return "Neutral"
    else:
        return "Negative"


# For Text data
def vader(text):
    #analyze the sentiment for the text
    scores = SentimentIntensityAnalyzer().polarity_scores(text)
    if scores['compound'] >= 0.05 :
        return "Positive"

    elif scores['compound'] <= - 0.05 :
        return "Negative"

    else :
        return "Neutral"


# For Text data
def text2emotion(text):
    emotion = dict(te.get_emotion(text))
    emotion = sorted(emotion.items(), key =
             lambda kv:(kv[1], kv[0]), reverse=True)
    emotionStr = list(emotion)[0][0]
    if(list(emotion)[1][1]>=0.5 or list(emotion)[1][1] == list(emotion)[0][1]):
        emotionStr+=" - {}".format(list(emotion)[1][0])
    print(emotion, emotionStr)
    return emotionStr


def imageEmotion(image):
    captured_emotions = emo_detector.detect_emotions(image)
    topEmotion = emo_detector.top_emotion(image)
    print(captured_emotions, topEmotion)
    img = image
    # font
    font = cv2.FONT_HERSHEY_SIMPLEX

    # fontScale
    fontScale = 1.2

    # Blue color in BGR
    color = (255, 0, 0)

    # Line thickness of 2 px
    thickness = 2
    for emotion in captured_emotions:
        x, y, w, h = tuple(emotion["box"])
        org = (x+w+4, y+5)
        emotions = emotion["emotions"]
        emotions = sorted(emotions.items(), key =
             lambda kv:(kv[1], kv[0]))
        cv2.rectangle(img, (x,y), (x+w,y+h), (0, 0, 255), 2)
        cv2.putText(img, emotions[len(emotions)-1][0], org, font,
                   fontScale, color, thickness, cv2.LINE_AA)
    return captured_emotions, topEmotion, img