Emotion Recognition of South Indian Regional Languages in Social Media

Sentiment analysis, which focuses on understanding people’s feelings, has become more popular recently. The main challenges in sentiment analysis include gathering large amounts of data, using the right methods or algorithms, and sorting it into different categories based on emotions. With the internet growing rapidly, social media has become a popular platform for people to share their feelings. Sentiment analysis using Twitter data, emphasizing the need for a comprehensive approach to understanding user sentiments. Leveraging Twitter APIs, gather tweets and employ TextBlob for sentiment scoring, classifying sentiments as positive, negative, or neutral. In a departure from conventional methods, this model incorporates emojis and images, broadening the scope of sentiment interpretation. It even determine sentiment scoring based on different slangs of different languages. Moreover, this paper shift focus from isolated words to analyzing the emotional context of entire sentences, enhancing the accuracy of sentiment classification.