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sentiment analysis - Coggle Diagram
sentiment analysis
Sentiment analysis is a valuable tool for various applications, including:
• Business Intelligence:
• Customer Feedback Analysis: Understanding customer opinions from reviews, surveys, and social media to improve products and services.
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Challenges
Sarcasm and Irony: Detecting when words are used to express the opposite of their literal meaning. E.g. “Oh great! Another delayed flight”
Negation: Understanding how words like "not" can flip the sentiment of a sentence. E.g. “ this is not a bad movie”
Context Dependence: The sentiment of a word or phrase can vary depending on the context. E.g. the phone has a small battery life”
Subjectivity and Nuance: Human language can be subtle, and expressing nuanced opinions can be challenging for algorithms.
Multipolarity: A single piece of text can express both positive and negative sentiments towards different aspects. E.g. "fantastic camera" and "long-lasting battery"
Evolving Language: New slang, abbreviations, and online language can be difficult for existing models to understand.
Cross-Lingual Sentiment Analysis: Sentiment analysis in languages other than English can be more complex due to linguistic differences and the availability of resources.
Advanced Techniques
Aspect-Based Sentiment Analysis (ABSA):
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Example: "The camera is great, but the battery is terrible."
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Multimodal Sentiment Analysis:
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Tooles
Python Libraries:
• NLTK: For tokenization, stemming, and lemmatization.
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• Transformers (Hugging Face): For deep learning-based sentiment analysis using BERT, RoBERTa, etc.
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Important for
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used in marketing, customer service, and social media monitoring
Approaches
Machine Learning (ML):
To train algorithms on labeled data (text with known sentiment) to automatically classify new text. Common ML algorithms include Naive Bayes, Support Vector Machines (SVM), Logistic Regression, and and Deep Learning models to capture complex patterns in text (like Recurrent Neural Networks, Long Short-Term Memory (LSTM), Transformers (BERT).).
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Tasks
• Polarity Classification: The most basic task is to classify the sentiment expressed in a text as positive, negative, or neutral.
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as opinion mining or emotion AI, is a field within Natural Language Processing (NLP) that aims to determine the emotional tone or subjective opinions expressed in text data.