☑️ Deep Learning: Deep learning is a subset of machine learning that involves artificial neural networks with multiple layers (deep neural networks). These networks are capable of learning complex patterns and representations from data, making them well-suited for tasks like sentiment analysis, where context and subtleties play a significant role.

☑️ Sentiment Analysis: Sentiment analysis is the task of classifying the emotional sentiment expressed in text data. It typically involves assigning a sentiment label to the text, such as "positive," "negative," or "neutral." More advanced sentiment analysis models can distinguish between fine-grained sentiments and emotions.

☑️ Deep Sentiment Analysis: Deep Sentiment Analysis leverages deep neural networks, often recurrent neural networks (RNNs), convolutional neural networks (CNNs), or more recently, transformer models (like BERT, GPT 3.5, GPT 4.0 or RoBERTa), to perform sentiment analysis with high accuracy and the ability to capture context and semantics effectively.

✍️ Deep Sentiment Analysis models are trained on large datasets containing text samples with sentiment labels, allowing them to learn the relationships between words, phrases, and sentiment expressions. These models can then be used to automatically analyze the sentiment of text data, which has numerous practical applications, such as:

☑️ Customer Feedback Analysis: Businesses can use deep sentiment analysis to automatically process and understand customer reviews, social media comments, or survey responses to gauge customer satisfaction and identify areas for improvement.

☑️ Stock Market Prediction: Investors and financial analysts can use sentiment analysis to analyze news articles, social media, and other textual data to predict stock market trends based on the sentiment expressed about specific companies or industries.

☑️ Political Opinion Monitoring: Governments and political organizations may use sentiment analysis to monitor public opinion and sentiment regarding political issues, candidates, and policies.

☑️ Brand Monitoring: Companies can track sentiment around their brand and products in real-time, helping them respond quickly to public perception and market trends.

☑️ Deep Sentiment Analysis is a valuable tool in natural language processing and machine learning, enabling automated understanding of human emotions and opinions in text data.

👉 Function Descriptions

✍️ The deep_emotion_analysis_survey_notes() function takes a single parameter text_data, which is a list of strings representing survey notes or comments to be analyzed for sentiment.

👉 It returns three lists:

🟢 sentiment_scores: A list of sentiment scores, typically ranging from -1.0 (indicating negative sentiment) to 1.0 (indicating positive sentiment).

🟢 sentiment_labels: A list of sentiment labels ("positive," "negative," or "neutral") corresponding to each survey note.

🟢 emotion_labels: A list of emotion labels (e.g., "joy," "anger," "sadness," "surprise") representing more fine-grained emotional states for each survey note.