Sentiment Analysis, also known as opinion mining, is a natural language processing (NLP) task that involves determining the sentiment expressed in a piece of text. The goal is to identify whether the text conveys a positive, negative, or neutral sentiment. Sentiment analysis has various applications, including social media monitoring, customer feedback analysis, and market research.
Here are some common approaches to sentiment analysis:
Rule-Based Approaches: Lexicon-based methods: Use predefined lists of words with associated sentiment scores. The overall sentiment of the text is calculated based on the presence and polarity of these words. Pattern-matching rules: Employ predefined rules or patterns to identify sentiment-bearing phrases or expressions. Machine Learning Approaches: Supervised learning: Train a model on a labeled dataset that includes examples of text with their corresponding sentiment labels (positive, negative, neutral). Common algorithms include Support Vector Machines (SVM), Naive Bayes, and neural networks. Unsupervised learning: Discover patterns and sentiments without labeled training data. Techniques like clustering and topic modeling can be applied. Hybrid Approaches: Combine rule-based and machine learning approaches to leverage the strengths of both methods. Deep Learning Approaches: Utilize deep neural networks, such as Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks, for capturing complex relationships and context in textual data. Aspect-Based Sentiment Analysis: Analyze sentiment at a more granular level by identifying sentiments towards specific aspects or entities mentioned in the text. When performing sentiment analysis, it’s important to consider the context, sarcasm, and ambiguity in language, as these factors can affect the accuracy of the analysis. Additionally, fine-tuning models on domain-specific data can enhance performance for particular applications.
Numerous tools and libraries are available for sentiment analysis, including NLTK, TextBlob, VADER, and various machine learning frameworks like scikit-learn and TensorFlow. Depending on your specific needs and the characteristics of your data, you can choose an approach or combination of approaches that best suits your application.
Here we will be using Rasa NLU for Sentiment Analysis. This can also be used for Classification under pre-defined labels.
Refer: https://threadwaiting.com/sentiment-analysis-using-rasa-nlu/