What is Sentiment Analysis?
Sentiment Analysis, also known as opinion mining or emotion AI, is a process that uses Natural Language Processing (NLP), computational linguistics, and machine learning to analyze digital text and determine the emotional tone of the message, which can be positive, negative, or neutral. It's a form of text analytics that systematically identifies, extracts, quantifies, and studies affective states and subjective information.
The process typically involves several stages. During the preprocessing stage, key words are identified to highlight the core message of the text. Techniques such as tokenization, which breaks a sentence into several elements or tokens, and lemmatization, which converts words into their root form, are used. Sentiment analysis uses machine learning models to perform text analysis of human language, designed to detect whether the overall sentiment of a piece of text is positive, negative, or neutral.
Sentiment analysis is an important business intelligence tool that helps companies improve their products and services by providing objective insights. It allows businesses to avoid personal bias associated with human reviewers by using artificial intelligence, resulting in consistent and objective results when analyzing customers' opinions. It's often used by businesses to detect sentiment in social data, gauge brand reputation, and understand customers.
Advanced sentiment analysis goes beyond polarity to detect specific feelings and emotions (angry, happy, sad, etc), urgency (urgent, not urgent) and even intentions (interested v. not interested). There are various other types of sentiment analysis, such as aspect-based sentiment analysis, grading sentiment analysis (positive, negative, neutral), multilingual sentiment analysis and detection of emotions.
What are some common techniques used in sentiment analysis?
Sentiment Analysis employs a variety of techniques to analyze and interpret the emotional tone of a given text. Here are some common techniques:
Machine Learning-Based Techniques — These techniques involve training an algorithm to identify relationships and patterns within labeled text data. The algorithm is fed a sentiment-labelled training set, and it learns to associate input data with the most appropriate corresponding label. Examples of machine learning algorithms used in sentiment analysis include Naive Bayes and Deep Learning LSTM.
Lexicon-Based Techniques — These techniques use a predefined list of words or phrases that have a certain sentiment, such as positive, negative, or neutral. The sentiment score of a text is determined by the presence of these words or phrases.
Linguistic Rules-Based Techniques — These techniques involve creating a set of manually-created rules. For example, a rule might state that any text containing the word “love” is positive. This approach includes NLP techniques like lexicons (lists of words), stemming, tokenization, and parsing.
Contextual Embedding — This technique involves understanding the context in which words are used, which can help improve the accuracy of sentiment analysis.
Hybrid Techniques — These techniques combine both lexicon-based and machine learning-based techniques to improve the accuracy of sentiment analysis. For instance, a machine-learned model that classifies text as positive, negative, and neutral could be combined with a rules-based approach that re-classifies certain words as negative.
Ensemble Techniques — These techniques combine multiple models or approaches to improve the accuracy of sentiment analysis.
What is the difference between rule-based and machine learning-based sentiment analysis?
Rule-based and machine learning-based sentiment analysis are two different approaches to understanding the emotional tone of a text.
Rule-Based Sentiment Analysis Rule-based sentiment analysis relies on a set of manually created rules to determine the sentiment of a text. These rules might be as simple as associating certain words with positive or negative sentiment, or they could involve more complex linguistic structures. For example, a rule might state that any text containing the word "love" is positive. This approach is simple and cost-efficient, but it lacks adaptability and can struggle with ambiguity and bias. If a sentiment is expressed in a way that doesn't match the predefined rules, the system may fail to correctly identify it. Rule-based systems are also deterministic, meaning they will always produce the same output for a given input, and they are easier to interpret and promote precision.
Machine Learning-Based Sentiment Analysis Machine learning-based sentiment analysis, on the other hand, involves training a model on a large dataset of text that has been labeled with sentiment. The model learns to associate certain features of the text with positive, negative, or neutral sentiment. This approach is dynamic and adaptable, capable of handling complex situations and continuously learning from data. Machine learning models are probabilistic, meaning they predict the likelihood of a sentiment based on the patterns they've learned from the training data. However, they require more data compared to rule-based models and can be more difficult to interpret.
Key Differences The key differences between these two approaches lie in their adaptability, scalability, and data requirements. Rule-based systems are less adaptable and scalable but require less data and are easier to interpret. Machine learning systems, on the other hand, are more adaptable and scalable, can handle complex situations better, but require more data and can be more difficult to interpret.
In practice, the choice between a rule-based and machine learning-based system often depends on the specific requirements of the sentiment analysis task at hand. Some systems even use a hybrid approach, combining rule-based and machine learning techniques to leverage the strengths of both.