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How to Understand Text Analytics

Text analytics is the process of extracting meaningful information from natural language text. It can help you perform tasks such as sentiment analysis, topic modeling, summarization, and more. In this post, I’ll explain some of the basic concepts and techniques behind text analytics, and how you can use them to analyze your own text data.

Tokenization

The first step in text analytics is to break down the text into smaller units called tokens. Tokens are usually words, but they can also be phrases, punctuation, or other symbols. For example, the sentence “we choose to go to the moon” can be tokenized into the following tokens:

we
choose
to
go
to
the
moon

Tokenization is important because it helps us identify the individual elements of the text and their frequency. It also prepares the text for further analysis, such as removing stop words, normalizing words, or generating n-grams.

Frequency Analysis

After tokenizing the text, we can count how often each token appears in the text. This can give us a clue about the main subject or theme of the text. For example, the most common words in the “go to the moon” speech are “new”, “go”, “space”, and “moon”. This suggests that the text is about space exploration and lunar missions.

Frequency analysis can also be done with n-grams, which are sequences of n tokens. For example, the most common bi-gram (two-word sequence) in the speech is “the moon”. This gives us more context and specificity than single words.

However, frequency analysis alone is not enough to capture the meaning and relevance of the text. Some words may appear frequently but have little significance, such as “the”, “a”, or “it”. These are called stop words, and they can be removed from the analysis. Other words may have different forms or spellings, such as “power”, “powered”, and “powerful”. These can be normalized or stemmed to reduce the number of tokens and increase the accuracy of the analysis.

Machine Learning for Text Classification

Another way to analyze text is to use machine learning algorithms to classify text into predefined categories. For example, we can use text classification to perform sentiment analysis, which is the task of determining whether a text is positive or negative. To do this, we need a set of labeled text data, such as restaurant reviews with ratings. We can then use the tokenized text as features and the ratings as labels to train a classification model. The model will learn the relationship between the tokens and the sentiment, and be able to predict the sentiment of new text.

Text classification can also be used for other tasks, such as topic modeling, spam detection, or document categorization.

Semantic Language Models

The most advanced technique for text analytics is to use semantic language models, which are models that capture the meaning and context of natural language. Semantic language models use embeddings, which are numerical representations of tokens in a high-dimensional space. Embeddings encode the semantic similarity and relationship between tokens, so that words that are related or have similar meanings are closer together in the space.

Semantic language models can support many different types of natural language processing tasks, such as text analysis, machine translation, summarization, and conversational AI. These tasks require not only understanding the words in the text, but also the intent, tone, and style of the text.

Conclusion

Text analytics is a powerful and versatile technique that can help you gain insights from natural language text. It involves various steps and methods, such as tokenization, frequency analysis, machine learning, and semantic language models. By applying these techniques, you can perform tasks such as sentiment analysis, topic modeling, summarization, and more. If you want to learn more about text analytics and how to use it in your own projects, check out the Azure AI Language service, which provides a range of text analytics capabilities and models.