Exploring Sentiment Analysis in Machine Learning and Its Use Cases.
What is Sentiment Analysis, how does it work behind the scenes and how does it apply to you?
Machine Learning and Artificial Intelligence has become an integral part of Computer Science. One major reason for this is that product consumers are increasingly embracing the idea of technology understanding them.
For example, I go on Amazon and order a product from a particular brand. If I am dissatisfied and leave a bad review, I want to achieve two things. I want to discourage other users from purchasing that product. I also want to tell Amazon I want to see less of that product, or less of that brand, in my shopping feed.
How exactly do all those reviews get processed? This turns into meaningful data Amazon can use to improve its services.
A common way they would achieve this is to use sentiment analysis. Sentiment analysis is a field of text analysis. It aims to determine the emotional tone of a piece of text.
Text analysis refers to the process of analysing and extracting meaningful insights from unstructured text data. One of the most important subfields of text analysis is sentiment analysis, which involves determining the emotional tone of the text. — DataCamp
Amazon can use sentiment analysis to determine how much of the reviews were positive, neutral, or negative. The type of text data determines the ideas used to create a sentiment analysis algorithm.
Lexicon-Based Analysis
You could look at lexicon-based analysis as an algorithm that goes through your text. It uses a set of defined rules or heuristics to process your text. This means it bases its analysis on lexical or syntactic features of the text.
In this type of analysis, you usually have a file that defines a set of rules. The rules help to calculate the positivity or negativity score for a word. The algorithm then cycles through your entire text and applies these rules to each of them.
The lexicon-based analysis is fairly easy to implement. But, it may not be as accurate as other methods used in sentiment analysis.
Machine Learning Analysis
This method involves training a model to identify the sentiment of a piece of text. It’s based on a set of labeled training data. In this case, we first of all give our model data on what positive and negative text will look like. We call this our training data. Our model would use training data to predict the positivity scores of other pieces of text.
Machine Learning can be more accurate than Lexicon-based analysis. However, it requires large amounts of labelled training data to achieve that amount of accuracy. Therefore, it can be more resource-heavy.
Pre-trained transformer-based deep learning
Deep Learning and Neural Networks are the foundational blocks of technology such as GPT-4 and BERT.
This approach involves using technologies trained on massive amounts of data. They can understand the meaning of the text and more accurately deduce if a piece of text is positive or negative.
This approach produces highly accurate data. However, it may not be the best approach in some cases. It is computationally expensive and best suited for serious sentiment analysis.
Preprocessing Your Text
Most times the text data does not come to us in a ready-to-use form. For us to feed the best quality data to our algorithm, we sometimes have to preprocess it.
Text preprocessing is a crucial step in performing sentiment analysis. It helps to clean and normalize the text data, making it easier to analyze. The preprocessing step involves a series of techniques. These techniques help transform raw text data into a form you can use for analysis. Some common text preprocessing techniques include tokenization, stop word removal, stemming, and lemmatization.
Noise
In the context of machine learning, noise refers to random or unpredictable fluctuations in data. It disrupts the ability to identify target patterns or relationships. The result is decreased accuracy or reliability of a model’s predictions or output.
Tokenization
Tokenization breaks text into smaller parts. It makes text easier for machines to analyze, and helps machines understand human language. Tokenization, in the realm of Natural Language Processing (NLP) and machine learning, refers to the process of converting a sequence of text into smaller parts, known as tokens.
Stop Words
Stop words are a set of commonly used words in a language. In English, common stop words include “a,” “the,” “is,” and “are.” Text Mining and Natural Language Processing (NLP) often use stop words to remove frequently used words that carry little useful information.
Stemming
Stemming, in Natural Language Processing (NLP), refers to the process of reducing a word to its word stem that affixes to suffixes and prefixes or the roots.
Lemmatization
Lemmatization is a natural language processing (NLP) technique that involves reducing words to their base or dictionary form, known as the “lemma.” Unlike stemming, which simply removes affixes from words to derive a root form, lemmatization takes into account the word’s part of speech and context to determine its canonical form. The resulting lemma represents the base meaning of the word and is a valid word found in a dictionary.
Lemmatization aims to normalize words while preserving their semantic integrity, making it a more linguistically accurate approach compared to stemming. By transforming words into their lemmas, lemmatization helps improve text analysis, information retrieval, and language understanding tasks in NLP.
In lemmatization, the resulting lemma represents the canonical or base form of a word, considering its part of speech and context. Lemmatization provides linguistically valid and meaningful lemmas, which can enhance the accuracy of text analysis and language processing tasks.
Applications of Sentiment Analysis
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Social media monitoring
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Customer support ticket analysis
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Brand monitoring and reputation management
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Product analysis
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Market research and competitive research
Conclusion
We learnt quite a lot about sentiment analysis, natural language processing and preprocessing text data. There is still a lot more to learn, especially if you want to apply it in a project.
If you would like to find out more about sentiment analysis, here are some great reads on that: